Podcasts about bayesian

Today's clip is from Episode 164, featuring Andrew Gelman, Aki Vehtari & Richard McElreath. In this conversation, Andrew explains why a Bayesian workflow goes far beyond simply fitting a model. He discusses the importance of building, fitting, and checking models, and why moving between simpler and more complicated models can reveal insights that a single model might miss. He also explores how simulation and generative modeling can help researchers evaluate new models and gain confidence in their results, even when there isn't an established method or published study to rely on. It's a look at why good statistical practice isn't just about getting an answer, but knowing how much you can trust it. Full discussion hereSupport & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free): Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome workTakeaways:Q: What is the "Bayesian Workflow" book about, and who is it for?A: It covers what the three authors know that isn't already in Bayesian Data Analysis (BDA3) or Statistical Rethinking, organized around case studies that walk through full analyses end to end rather than just giving a recommendation. It's not an introduction to Bayesian inference -- it assumes you already know the basics -- but a guide to making theoretically informed, professional decisions at the many branching points a real analysis involves that source books rarely acknowledge.Q: What's a concrete way to report Bayesian results without just handing over a posterior distribution?A: Report a few named scenarios from the distribution, such as pessimistic, median, and optimistic. This is easier to discuss than a full posterior and helps shift the conversation toward what would move outcomes from the median toward the optimistic case.Chapters:00:00:00 Who are this episode's three guests, and what is Bayesian Workflow?00:02:35 What's new: the LBS Instagram account and the Carnegie Mellon workshop?00:05:22 What is Richard McElreath's origin story, from anthropology to statistics?00:18:22 What is the elevator pitch for the Bayesian Workflow book?00:20:12 Where does workflow sit between statistical theory and case studies?00:27:21 Why express your scientific background in a generative model?00:35:04 What came out of the LBS listener contest?00:36:43 How is a Bayesian workflow different from a pipeline?00:39:03 What is reverse Bayes, and how does it help with prior sensitivity?00:43:53 How do Bayesians reinterpret non-Bayesian methods?00:45:02 How is the Bayesian Workflow book structured?00:46:51 Who is Dorota, the LBS contest grand prize winner?00:52:24 When does a hierarchical model stop being an innocuous assumption?00:58:17 Can multilevel regression and poststratification pool detection across sites?00:59:32 Why start with a big generative simulation before the statistical model?01:02:05 What is the "secret weapon" of comparing shrinkage to fixed-effects estimates?01:11:02 How do you detect which assumptions are actually driving your inference?01:15:24 How do you get regulated industries to accept a posterior instead of a score?01:22:04 Should statisticians soften uncertainty for decision makers?01:23:11 Why report three scenarios instead of a single number?01:25:16 How do you communicate survival probabilities to cancer doctors?01:27:51 How do you handle a leaky instrument in causal inference?01:29:16 What is a principal stratification model?01:34:47 What are the three authors working on next?01:39:53 If you had unlimited time and resources, which problem would you solve?01:41:26 Could statistical workflow be made more axiomatic?01:42:03 Which great scientific mind would you have dinner with?Thank you to my Patrons for making this episode possible!Links from the show

In this episode of Communicable, Amy Legg (Brisbane, Australia), Rekha Pai Mangalore (Melbourne, Australia) & Angela Huttner (Geneva, Switzerland) return for the third part of the ‘We will make you love PK/PD' series. The hosts are joined by two experts, Amanda Gwee (Melbourne, Australia) & Sebastian Wicha (Hamburg, Germany), to dive into an important application of PK/PD: model-informed precision dosing (MIPD), and how it allows for more informed, individualised dosing decisions. The episode also covers the open-access educational tools, KidsCalc and TDMx, developed by Gwee and Wicha respectively, and the steps clinicians can take to put MIPD into practice, keeping the individual patient at the centre of dosing decisions.Resources TDMx, https://www.tdmx.eu/KidsCalc, https://www.kidscalc.org/Further readingBroeker A, et al. Towards precision dosing of vancomycin: a systematic evaluation of pharmacometric models for Bayesian forecasting. CMI 2019. DOI: 10.1016/j.cmi.2019.02.029Brusamarello C, et al. How important are MIC determination methods when targeting vancomycin levels in patients with Staphylococcus aureus infections? J Antimicrob Chemother 2021. DOI: 10.1093/jac/dkab065De Cock PA, et al. Bedside model-informed precision dosing of vancomycin in severely ill neonates and children in Belgium (the BENEFICIAL trial): a multicentre, randomised controlled trial. Lancet Child Adolesc Health 2026. DOI: 10.1016/S2352-4642(25)00385-2  Gwee A, et al. Identifying a therapeutic target for vancomycin against staphylococci in young infants. J Antimicrob Chemother 2022. DOI: 10.1093/jac/dkab469Haiping X, et al. Model-informed precision dosing of vancomycin in children 3 months to 18 years of age using Australia-wide data. Antimicrob Agents Chemother 2026. DOI: 10.1128/aac.01840-25  Harwood K, et al. Refining Absolute GFR Estimation in Children: Development and Validation of the GROW-GFR Equation. Front Pharmacol 2026. Minichmayr IK, et al. Model-informed precision dosing: State of the art and future perspectives. Adv Drug Deliv Rev 2024. DOI: 10.1016/j.addr.2024.115421Starp J, et al. Towards model-informed precision dosing of intravenous linezolid: a multicentre external evaluation of pharmacokinetic models in critically ill adults. CMI 2026. DOI: 10.1016/j.cmi.2025.08.032Wilkins AL, et al. Individualized vancomycin dosing in infants: prospective evaluation of an online dose calculator. Int J Antimicrob Agents 2023. DOI: 10.1016/j.ijantimicag.2023.106728 US National Kidney Foundation for different equations for kidney function estimates as related to drug dosing – estimating kidney function for medication related decisions: Frequently Asked Questions | National Kidney Foundation

This week Bruce takes a deep dive into Pascal's Wager. If there is even a small chance that God exists, and believing could lead to an infinite reward while disbelief risks an infinite loss, then believing is the rational choice... right?Blaise Pascal was one of the founders of probability theory back in the 17th century. Even aside from the potentially problematic theological point his famous wager makes, it also raises important questions about probability theory, Bayes' theorem, and even modern Bayesian epistemology. How do we handle situations where the potential payoff is enormous—or even infinite?Pascal's Wager is also the thread we pull that unravels Bayesian Epistemology, while also showing why Bayesian Reasoning is really part of Critical Rationalism.⁠⁠⁠⁠⁠⁠⁠⁠⁠Support us on Patreon⁠⁠⁠⁠⁠⁠⁠⁠

Jordi Visser is a veteran macro investor with 30+ years of experience and the author of the VisserLabs Substack. In this conversation, we break down why the AI trade already bottomed, how bitcoin fits into the future of markets, and why companies like Figure Technologies are outgrowing legacy banks like JPMorgan. We also discuss inflation, the AI IPO wave with Anthropic and OpenAI, and how AI is reshaping everyday life — from raising kids to running a household.====================Arch Public is an agentic trading platform that automates investment strategies across Stocks, Commodities, ETFs and Crypto. Whether you're rotating into AI & Gold, allocating to the S&P 500, or accumulating Bitcoin, Arch Public executes your plan 24/7 without ever taking custody of your assets or funds. Sign up today at https://www.archpublic.com, and start your FREE automated trading strategy! ====================GalaxyOne is a financial technology platform built for people who want their cash working harder. Open an account with promo code POMP and deposit $10,000 to earn a $3,000 bonus. See site for promotion details → https://go.galaxy.app/HMiq/p57n69yy Galaxy Premium Yield is an investment note issued by Galaxy Digital LP and guaranteed by Galaxy Digital Holdings LP. It is not a bank deposit, is unsecured, and is not FDIC or SIPC insured. U.S. accredited investors only. Cash deposits held at Cross River Bank, Member FDIC. Securities products are not FDIC insured, not bank guaranteed, and may lose value. GalaxyOne Crypto is not FDIC or SIPC insured. Terms apply.====================0:00 - Intro0:50- Why stocks keep climbing despite AI fears2:41 - Jordy's AI portfolio & calling market bottoms7:05 - Inflation report & the Bayesian mindset11:27 - Figure Technologies & the tokenization boom19:47 - Why JPMorgan and legacy banks are at risk24:18 - Bitcoin is the "S&P 500 of ten years from now"26:14 - Anthropic, OpenAI & the AI IPO wave31:25 - AI goes mainstream: his wife & raising kids in the AI era42:43 - ICE deportations & the inflation debate47:47 - Politics, protests & the AI data center fight55:50 - Jordi's weekly videos & how you can help him

Summary In this episode, Andy sits down with Owen Fitzpatrick, a psychologist, speaker, and author of Inner Propaganda: Leading Hearts and Minds through Turbulent Times. Owen has spent close to 30 years studying how beliefs form and change, interviewing people everywhere from North Korea to Rwanda to Afghanistan. His thesis is unsettling: our brains do not simply take in facts and reach objective conclusions. They build a story we then experience as reality. Owen and Andy work through what that means on real projects. You'll hear how a warning from a colleague can quietly harden into a conviction about a teammate, and how Bayesian reasoning gives you a way out. You'll learn Owen's five types of truth, how to tell courageous conviction from dangerous denial, and what leaders can actually make stable when they cannot promise a stable outcome. Owen also explains why pushing harder for buy-in is often the very reason people resist, and how an antifragile identity helps teams face uncertainty like AI without denial or panic. If you're looking for a fresh way to think about belief, influence, and leading through turbulent times, this episode is for you! Sound Bites "We all live in that world where we think we're the one person that isn't the victim of propaganda, and my point or my thesis is we're all victims of our own inner propaganda." "We're not necessarily just convinced by others. We convince ourselves." "Because I think when we say, 'I'm no good at something,' we lock ourselves into it." "Well, if you're not great at communicating with people, get great." "Most of the time our beliefs just create a sort of a reality for us, and that reality can help us or harm us." "So our brains are prediction machines." "Whenever we talk about belief, believing in your ability to succeed in the future is critical if you want to succeed in the future, but that doesn't mean you deny the present." "It, it's not that we think negatively, it's we believe negatively, and we see the world through those lenses." "So I think we want to be able to challenge our beliefs and build a bit more and get more comfortable with uncertainty away from the table, but when we're at the table with our team, that's when we bring the certainty." "We like the idea that we are making this decision of our own free will." "And when you look at what a belief is, a belief is an idea we feel certain about, and that word feel is the most important word of that sentence." "It's okay to believe less in certain things. It's okay to believe more in certain things. It's okay to believe better." Chapters 00:00 Introduction 02:29 Start of Interview 02:40 The Belief About Himself Owen Took Too Long to Update 05:56 A Sweaty Debate Speech and What Came After 08:28 Not Good at Something Is a Skill Gap, Not an Identity 09:45 The Belief Growth Mindset 12:55 The Sandra Story: When a Warning Becomes a Conviction 14:07 Why the Brain Craves Cognitive Closure 17:15 Using Bayesian Reasoning to Loosen a Belief 21:00 Melanie Perkins and Alan Mulally: Conviction or Denial? 21:36 The Five Types of Truth 26:24 Just Because It Is Your Truth Does Not Make It True 28:41 Where Objective Truth Actually Belongs 31:47 What "Leadership Is Propaganda" Does Not Mean 34:45 Why Change Management and AI Adoption Stall 36:30 Why Owen Chose Propaganda Over Self-Talk 37:57 Do I Believe It Because It Makes Me Look Good? 38:49 Answering the AI Question Without Denial or Hype 40:15 Building an Antifragile Identity 44:27 Certainty Is Contagious, and When to Change Course 45:20 What Leaders Can Make Stable in a Volatile Period 47:45 Control What You Can, Influence What You Can, Accept the Rest 51:01 When Pushing for Buy-In Creates the Resistance 52:45 You're Asking the Wrong Question About Persuasion 54:30 Reading the Person Before You Make the Ask 57:30 Helping Kids Hold Strong Beliefs Without Contempt 1:01:46 End of Interview 1:02:15 Andy Comments After the Interview 1:05:09 Outtakes Learn More You can learn more about Owen and his work at InnerPropaganda.com. For more learning on this topic, check out: Episode 370 with Chantel Prat. One of the smartest, clearest, and funniest books on the brain, and why all of it matters for how you lead. Episodes 59 and 60 with Cathy Davidson. She explains how the brain science of attention changes everything. Episode 32 with Brad Kolar. A look at the direct implications of neuroscience for leadership. Owen's TED Talk, available here on YouTube. Chat with PMeLa You can chat directly with PMeLa, the podcast's AI persona, to get episode recommendations and answers to your project management and leadership questions. Visit PeopleAndProjectsPodcast.com/PMeLa to chat with her. Join Us for LEAD52 I know you want to be a more confident leader–that's why you listen to this podcast. LEAD52 is a global community of people like you who are committed to transforming their ability to lead and deliver. It's 52 weeks of leadership learning, delivered right to your inbox, taking less than 5 minutes a week. And it's all for free. Learn more and sign up at GetLEAD52.com. Thanks! Thank you for joining me for this episode of The People and Projects Podcast! Talent Triangle: Power Skills Topics: Leadership, Belief, Persuasion, Influence, Uncertainty, Change Management, Growth Mindset, Psychological Reactance, Buy-In, Artificial Intelligence, Resilience, Project Management The following music was used for this episode: Music: The Fantastical Ferret by Tim Kulig License (CC BY 4.0): https://filmmusic.io/standard-license Music: Funny by Frank Schroeter License (CC BY 4.0): https://filmmusic.io/standard-license

Today's clip is from episode 160, featuring Vaden Masrani. In this conversation, Vaden explores the tension between Bayesian statistics and Bayesian epistemology, and why he sees them as fundamentally different.He explains why Bayesian epistemology can run into problems when trying to explain where hypotheses themselves come from, and argues that an emphasis on finding supporting evidence can encourage confirmation bias rather than genuine scientific inquiry. He also discusses Hempel's paradox, Popper's idea of falsification, and why these philosophical problems don't necessarily undermine Bayesian statistics itself.Full discussion hereSupport & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free): Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

All Else Equal is taking a short break over the summer and will be revisiting some of our favorite conversations from last season. The show will be back with new episodes soon.  Whether it be in politics, public health, or corporate finance, why are people more likely to interpret facts or data in a way that fits their preconceived notions about the world as opposed to searching for the fundamental truth?   A new paper from the Harvard Business School called, Sharing Models to Interpret Data (by Joshua Schwartzstein and Adi Sunderam) studies the propensity for people to adopt interpretations to data based on their community's beliefs, and why this can lead to less accurate conclusions. Hosts and finance professors Jonathan Berk and Jules van Binsbergen are joined by the paper's co-author  Adi Sunderam, who is a professor of corporate finance at Harvard Business School, a research associate at the National Bureau of Economic Research, and a co-editor of the Journal of Finance.  The conversation covers the complexity of Bayesian updating and how the process is improperly deployed in today's thinking, not only in corporate decision-making but also on a sociological level. They also discuss Sunderam's model for explaining how people interpret data, why people are more likely to fall into group-belief dynamics, and if there are any interventions that would lead to better decision-making.   Read Adi Sunderam and Joshua Schwartzstein's paper:  Sharing Models to Interpret Data   Find All Else Equal on the web:   https://lauder.wharton.upenn.edu/allelse/   All Else Equal: Making Better Decisions Podcast is a production of the UPenn Wharton Lauder Institute through University FM.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Scientific Sense ® by Gill Eapen: Prof. Harald Uhlig is Professor of Economics at the University of Chicago. His research focuses on macroeconomics, monetary economics, financial markets, and Bayesian time series analysis, particularly at the intersection of macroeconomics and financial economics. Please subscribe to this channel:https://www.youtube.com/c/ScientificSense?sub_confirmation=1

This week on the Excess Returns Weekly Wrap, Jack Forehand and Matt Zeigler break down the AI capital spending boom, the risk that data center investment is crowding out housing and other parts of the economy, and what that means for markets. Featuring Richard Bernstein, David Rosenberg, Tian Yang, and Brent Donnelly, the episode covers AI CapEx, GDP growth, inflation, the K-shaped economy, AI ROI, and why rationality and Bayesian thinking matter more than raw intelligence for investors and traders.Topics coveredWhy the AI and data center boom may be misallocating capital away from housing and infrastructureWhat the dot-com bubble taught Richard Bernstein about investing where capital is scarceWhy AI related spending is approaching half of business CapEx while ex-AI investment is shrinkingHow today's K-shaped economy differs from the broad economic boom of the late 1990sThe difference between AI's contribution to GDP growth and its share of total GDPTian Yang's Kalecki-Levy framework for understanding spending, savings, income, and economic resilienceWhy a pullback in hyperscaler CapEx could weaken the spending and income loopWhy AI return on investment is so difficult to measure and how the profit pool could broaden beyond hardwareBrent Donnelly on why rationality and flexibility matter more than credentials or raw intelligenceWhy persistent bearishness can become a major investing mistakeHow Bayesian thinking, position sizing, and changing your mind help investors stay in the gameTimestamps00:02 Rich Bernstein and David Rosenberg reunite and this week's lineup04:10 The dot-com lesson: what happens when capital floods one sector08:15 AI CapEx, inflation, and why today's economy is different from the 1990s13:58 Kalecki-Levy: how spending and savings are keeping growth resilient18:03 AI CapEx concentration, productivity, and the uncertainty around ROI22:21 Brent Donnelly on why rationality beats intelligence26:21 Strong opinions, flexibility, and Bayesian thinking30:33 What traders and market makers can teach long-term investorsLearn more about the Excess Returns podcast network:https://excessreturns.coNo information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.

Your marketing engine is generating leads, but how do you know which ones are actually worth handing to sales? That is the question at the heart of this episode, and it is one that gets harder to answer the bigger your funnel gets.My guest is Nick Panayi, a technology marketing veteran who most recently led marketing at Inovalon. If you listened to our episode with Nick last year, you know he has spent his career at the intersection of marketing and AI. This time he is back to walk me through something he built with his partner Chris Marin: an AI-powered lead scoring system that moved his team's MQL-to-opportunity conversion rate from 10 percent to 17 percent in a single quarter. That is a 70 percent improvement in lead conversion.We get into why traditional lead scoring has not really changed since marketing automation platforms first showed up, and why that single-dimensional approach creates false positives and quietly erodes trust between marketing and sales. Nick breaks down how his team combined ten different signals, from individual behavior to ICP alignment to lookalike modeling, into one dynamic score built on a self-improving Bayesian model.We also talk about the part I think matters most for actually getting sales to act: pairing every score with a plain-language, AI-generated synopsis that tells the rep exactly why the lead matters and how to open the conversation.If you are a CMO or VP of marketing trying to get sales to actually believe in your leads, this episode gives you a real blueprint, along with Nick's honest take on what it takes to get started even if you do not have a full AI team in-house.Key Topics Covered"(00:00)" Welcome and introduction"(05:00)" Nick's background "(06:00)" Why traditional lead scoring has not changed in years "(09:00)" The specific problems with lead scoring"(12:00)" Multivariate AI platform scoring"(15:00)" Turning complex scoring data into a single AI generated synopsis "(17:00)" Why recency and lead quality together drive better conversion"(19:00)" How the project actually got built"(21:00)" The result"(23:00)" A real example of a counterintuitive lead score"(25:00)" The value of being able to reconstruct a full deal story"(27:00)" Nick's one piece of advice for a CMO who wants to get started next week"(31:00)" Adam's five key takeaways from the conversationIf you are interested in discussing this or any other topic, let's have a chat.  Reach out to me directly to schedule a no-obligation discussion. This isn't a sales call, but rather an opportunity to talk through your questions and challenges.Follow me on LinkedIn.Subscribe to The Healthtech Marketing Show on Spotify or watch us on YouTube for more insights into marketing, AI, ABM, buyer journeys, and beyond!Find all of our episodes on the Health Podcast Library.Thank you to our presenting sponsor, HealthcareNOW, 24/7 expert shows, interviews, and podcasts, powering healthcare leaders with innovation, policy, and strategy insights.

Today's clip is from episode 160, featuring Vaden Masrani. In this conversation, Vaden lays out a sharp critique of Bayesian epistemology - the roughly hundred-year-old philosophical tradition, popular in some Oxford-adjacent circles, that treats subjective probability estimates as legitimate even when there's no data behind them.Vaden's core objection: doing Bayes' theorem on numbers you made up in your head is like fitting a regression line to an empty scatter plot - the math looks rigorous, but there's nothing underneath it. He argues this "math-washing" can trick people into thinking a decision is well-informed simply because it's dressed up in probability language, when frequentists and data-driven Bayesians alike would say the same thing: no data, no model.Get the full discussion hereSupport & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free): Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

******Support the channel******Patreon: https://www.patreon.com/thedissenterPayPal: paypal.me/thedissenterPayPal Subscription 1 Dollar: https://tinyurl.com/yb3acuuyPayPal Subscription 3 Dollars: https://tinyurl.com/ybn6bg9lPayPal Subscription 5 Dollars: https://tinyurl.com/ycmr9gpzPayPal Subscription 10 Dollars: https://tinyurl.com/y9r3fc9mPayPal Subscription 20 Dollars: https://tinyurl.com/y95uvkao ******Follow me on******Website: https://www.thedissenter.net/The Dissenter Goodreads list: https://shorturl.at/7BMoBFacebook: https://www.facebook.com/thedissenteryt/Twitter: https://x.com/TheDissenterYT This show is sponsored by Enlites, Learning & Development done differently. Check the website here: http://enlites.com/ Dr. Wataru Toyokawa is Unit Leader at the Computational Group Dynamics (COGNAC) Collaboration Unit at RIKEN CBS, Tokyo, and Visiting Scientist at Center for Advanced Study of Collective Behaviour at the University of Constance. His research focuses on the computational underpinnings and eco-evolutionary implications of human social learning and their relationships with group decision-making and collective behavior. Using computational modelling and Bayesian statistical methods, coupled with online real-time behavioral experimentation, he is quantitatively approaching human social behavior and group dynamics. He also uses mathematical models to study population dynamics and evolutionary games. In this episode, we start by talking about social learning: what it is, and how it is studied. We discuss why we learn from others, when we copy others, and how we learn from others despite our individual differences. We then talk about collective decision-making, and discuss the madness and wisdom of crowds, and the social learning strategies that regulate the wisdom and madness of interactive crowds.--A HUGE THANK YOU TO MY PATRONS/SUPPORTERS: PER HELGE LARSEN, BERNARDO SEIXAS, ADAM KESSEL, MATTHEW WHITINGBIRD, ARNAUD WOLFF, TIM HOLLOSY, HENRIK AHLENIUS, ROBERT WINDHAGER, RUI INACIO, ZOOP, MARCO NEVES, COLIN HOLBROOK, PHIL KAVANAGH, SAMUEL ANDREEFF, FRANCIS FORDE, TIAGO NUNES, FERGAL CUSSEN, HAL HERZOG, NUNO MACHADO, JONATHAN LEIBRANT, JOÃO LINHARES, STANTON T, SAMUEL CORREA, ERIK HAINES, MARK SMITH, JOÃO EIRA, TOM HUMMEL, SARDUS FRANCE, DAVID SLOAN WILSON, YACILA DEZA-ARAUJO, ROMAIN ROCH, YANICK PUNTER, CHARLOTTE BLEASE, NICOLE BARBARO, PAWEL OSTASZEWSKI, NELLEKE BAK, GUY MADISON, GARY G HELLMANN, SAIMA AFZAL, ADRIAN JAEGGI, JOÃO BARBOSA, JULIAN PRICE, HEDIN BRØNNER, FRANCA BORTOLOTTI, GABRIEL PONS CORTÈS, URSULA LITZCKE, SCOTT, ZACHARY FISH, TIM DUFFY, SUNNY SMITH, JON WISMAN, WILLIAM BUCKNER, LUKE GLOWACKI, GEORGIOS THEOPHANOUS, CHRIS WILLIAMSON, PETER WOLOSZYN, DAVID WILLIAMS, DIOGO COSTA, ALEX CHAU, CORALIE CHEVALLIER, BANGALORE ATHEISTS, LARRY D. LEE JR., OLD HERRINGBONE, DAN SPERBER, ROBERT GRESSIS, JEFF MCMAHAN, JAKE ZUEHL, MARK CAMPBELL, TOMAS DAUBNER, LUKE NISSEN, KIMBERLY JOHNSON, JESSICA NOWICKI, LINDA BRANDIN, VALENTIN STEINMANN, ALEXANDER HUBBARD, BR, JONAS HERTNER, URSULA GOODENOUGH, DAVID PINSOF, SEAN NELSON, MIKE LAVIGNE, JOS KNECHT, LUCY, MANVIR SINGH, PETRA WEIMANN, CAROLA FEEST, MAURO JÚNIOR, TONY BARRETT, NIKOLAI VISHNEVSKY, STEVEN GANGESTAD, TED FARRIS, HUGO B., JORDAN MANSFIELD, CHARLOTTE ALLEN, PETER STOYKO, DAVID TONNER, LEE BECK, PATRICK DALTON-HOLMES, NICK KRASNEY, RACHEL ZAK, DENNIS XAVIER, CHINMAYA BHAT, RHYS, ALEX MACLEOD, HAIDAR, JULIEN PORCHER, ROBERT SUNDSTRÖM, JON STEWART, AND JENNY M!A SPECIAL THANKS TO MY PRODUCERS, YZAR WEHBE, JIM FRANK, ŁUKASZ STAFINIAK, TOM VANEGDOM, BERNARD HUGUENEY, CURTIS DIXON, THOMAS TRUMBLE, KATHRINE AND PATRICK TOBIN, JONCARLO MONTENEGRO, NICK GOLDEN, CHRISTINE GLASS, IGOR NIKIFOROVSKI, PER KRAULIS, ADAM HUNT, ANTHONY DI LORENZO, AND JOÃO BARBOSA!AND TO MY EXECUTIVE PRODUCERS, MATTHEW LAVENDER,SERGIU CODREANU, AND GREGORY HASTINGS!

Brent Donnelly joins Matt Zeigler to explain how professional traders build a durable edge through risk management, trading psychology, probabilistic thinking, and creative market analysis.Drawing from his new book, Trade Outside the Box: Advanced Thinking for Professional Traders, Brent breaks down why trading strategies decay, why rationality beats intelligence, how to avoid risk of ruin, and how lessons from poker, behavioral finance, and real-world experience can improve decision-making.Trade Outside the Box: Advanced Thinking for Professional Tradershttps://amzn.to/4h9bi3eBrent Donnelly on Xhttps://x.com/donnelly_brentSpectra Marketshttps://www.spectramarkets.comTopics covered:Why fundamentals, technical analysis, behavioral finance, and quantitative methods are necessary but not sufficient for trading successHow traders can develop an edge by connecting markets to poker, psychology, biology, auto racing, and video gamesWhy profitable trading strategies decay as more investors discover and copy themHow changing volatility regimes force traders to adapt their style and avoid becoming a one-trick ponyWhy mismatching a long-term investment thesis with a short-term stop loss can destroy a good ideaHow trading journals and P&L data help separate normal variance from a broken processWhy the house money effect can make traders more reckless after large gainsWhy rationality, flexibility, and expected value matter more than credentials or raw intelligenceHow Bayesian thinking helps traders update probabilities and fight confirmation biasThe difference between independent thinking and blind contrarianismWhy avoiding risk of ruin, protecting family and health, and defining success beyond money are essential to a sustainable trading careerTimestamps:00:00 Introduction to Brent Donnelly and Trade Outside the Box04:00 Why smart analysts often produce fully priced trade ideas08:00 Poker discipline and avoiding boredom trades12:00 How lead-lag correlation trading lost its edge16:35 Matching a trade's stop loss to its time horizon21:00 What trading data reveals about win rates and expected value25:00 The house money effect and the danger of overearning29:00 Why rational traders beat smarter traders33:00 Strong opinions weakly held and Bayesian updating37:00 Curating a balanced diet of bullish and bearish information41:00 Using creativity and outside disciplines to find market edge45:11 Avoiding risk of ruin and the lessons of Jesse Livermore50:29 The Serenity Prayer and focusing on what traders can control55:00 Choosing family and health over markets59:00 Why your first thought may not be your ownLearn more about the Excess Returns podcast network:https://excessreturns.coNo information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms, or their clients.

Today's clip is from episode 162, featuring Chris Krapu. In this conversation, Chris explains why Bayesian thinking remains surprisingly valuable in today's AI landscape - even when the models themselves aren't explicitly Bayesian.Rather than uncertainty estimation, Chris highlights a different advantage: Bayesian training provides a deep intuition for concepts like priors, sampling, rejection sampling, and high-dimensional geometry, making it much easier to understand and apply modern AI research. He also discusses why Bayesian methods are becoming increasingly relevant for evaluating agentic AI systems, where complex workflows and limited evaluation data make hierarchical models and sensible priors especially powerful.Get the full discussion hereSupport & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free): Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Two theoretical physicists helped design landmark surveys of 1,600+ scientists on the deepest unsolved questions in physics — and found almost no consensus at all. From string theory's shockingly low support to physicists admitting their models run on "belief," this episode exposes the faith hiding inside science. Adam Frank (University of Rochester astrophysicist and astrobiologist) and Niayesh Afshordi (Perimeter Institute / University of Waterloo cosmologist, co-author of the APS Physics Magazine "Big Mysteries" survey) join Brian to unpack what physicists actually believe versus what they can prove. We cover: - Why string theory pulled a shockingly small share of the vote against loop quantum gravity - What a Bayesian "prior" reveals about every scientist's hidden beliefs - Why one branch of physics quietly became unfalsifiable - How sociology and "tastemakers" can hijack scientific consensus - Why AI might become the field's unlikely savior. There is no sane statistical analysis that doesn't have a prior — that's your belief. Timestamps: 00:00 – Why scientists owe the public real answers 04:00 – Splitting time between research and outreach 07:55 – The survey that "rankled" Brian Keating 09:18 – Are physicists secretly just like Spock? 12:05 – Are we living in an anti-scientific age? 16:56 – Why most scientists refuse to go public 21:36 – Should "belief" ever appear in a survey? 23:12 – Is string theory really 21st-century physics? 26:20 – When sociology hijacks scientific consensus 28:39 – The hidden "prior" behind every experiment 33:01 – String theory's shockingly low vote count 39:03 – Four levels of belief, from data to faith 41:39 – Inside the 1,675-physicist mystery survey 46:12 – Kingmakers, tastemakers, and physics cliques 54:12 – Is advanced tech indistinguishable from magic? 59:23 – Could AI become physics' long-awaited savior? 1:01:47 – What's next for Adam and Niayesh ———

Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome workTakeaways:Q: How does putting a Gaussian process on unknown coordinates fix noisy location data in mineral prospecting?A: In mining and geostatistics, the classic Gaussian process model, known there as kriging, assumes you know exactly where each sample was taken. Chris' project broke that assumption on purpose: the recorded coordinates for each core sample were only accurate to within a rough radius. By treating the true locations as latent variables and putting a Gaussian process over them jointly with the measurements, the model could still reconstruct the underlying gold-concentration field, even though the exact sampling locations were never known precisely. It's a demonstration that Gaussian processes can absorb structural uncertainty that looks, at first glance, like it should make the problem impossible.Q: What is "Poverty Bayes," and what did it cost to train a two-million-parameter Bayesian model?A: Poverty Bayes was Chris' experiment in seeing how cheaply a large Bayesian model could be trained using modern cloud infrastructure. He fit a hierarchical logistic regression with close to two million parameters, using PyMC's Hamiltonian Monte Carlo on a single A100 GPU rented through Modal, a serverless platform that deploys a Python script straight to GPU hardware with almost no setup. He'd originally guessed it would cost around five dollars, the price of a Big Mac, but the real bill came in an order of magnitude lower. A model that would take a Gibbs sampler weeks to run, and that once required a research lab's dedicated GPU, now costs pocket change and a few minutes of setup.Q: What's the current bottleneck in Bayesian-at-scale tooling?A: Chris argues the software has largely caught up: PyMC's JAX backend and NumPyro make GPU-accelerated Bayesian modeling work out of the box for most problems. What's missing is common knowledge. Companies are clearly running large Bayesian models in production, but the results stay behind corporate firewalls. Chris' proposal is a community benchmark effort: which frameworks handle a million-parameter Markov random field on a given GPU out of the box, since this kind of expensive, slow-running benchmark is a poor fit for standard CI pipelines but valuable for the field to know.Chapters:22:57 When does GPU acceleration actually pay off for a Bayesian model?26:33 What did it cost to train a two-million-parameter model on Modal?30:36 What happened when Chris asked 200 different LLMs to flip a coin?34:50 Where do Bayesian ideas show up in the agentic AI systems Chris builds at Nvidia?40:16 Are statisticians being made obsolete by large language models?41:19 How does putting a Gaussian process on unknown coordinates fix noisy data in mineral prospecting?58:05 What is Chris looking forward to working on next?Thank you to my Patrons for making this episode possible!Links from the show here

Today's clip is from episode 161, featuring Luigi Acerbi. In this conversation, Luigi explains one of the biggest engineering bottlenecks facing transformer-based probabilistic models—and how his group found a way around it.The core challenge is that many inference models treat data as an unordered set, making them naturally permutation invariant. That's statistically elegant, but computationally painful: every time a new data point arrives, the model has to recompute attention over the entire dataset from scratch, preventing the kind of KV caching that makes modern language models so efficient.Luigi walks through his team's solution: a hybrid architecture that keeps the original context fully set-based while introducing a causal-attention buffer for newly arriving data. The result is dramatically faster inference- up to 100× faster in some settings - opening the door to applications like reinforcement learning, active data acquisition, and, ultimately, Luigi's long-term vision of a foundation model for Bayesian inference.Get the full discussion hereSupport & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work

Can you find a real edge in prediction markets? Simon hunts the arbitrage — and calls Netflix cheap. In this week's WorldWideMarkets: •

Hello Interactors,As Canadian and Alaskan wildfire smoke drifts across North American borders, it's easy to resort to feelings and language of crisis in the demand for urgent, immediate control. But fires are not new to these landscapes, and this “crisis” sits alongside others that are also on fire. Climate crises events bring into focus which histories we forget, whose knowledge we ignore, and which relationships we disrupt. Turns out it's happening at a cellular level too. The real danger is a world and ecology changing so quickly, and understood so narrowly, that it erases the very temporal patterns, memories, and ecological relationships that make adaptation possible.FIRE'S FRACTURED FREQUENCYThe climate crisis is often communicated through rising averages. We read of climbing global temperatures, sea levels, atmospheric carbon dioxide concentrations, and acres burned. These measurements are indispensable, but averages can make crisis feel distant and abstract. In a forest, climate change is also experienced as an altered interval — as too little time between one disturbance and the next.A forest is not a passive surface. It actively shapes its own microclimate, stores carbon, and retains moisture until fire temporarily disrupts these relationships…and in doing so creates new ones.To understand what shorter fire intervals are doing to Alaska's boreal forests, ecologist Xanthe Walker and an interdisciplinary team of researchers examined carbon storage and forest recovery across 555 plots associated with thirty-one fires. They compared stands with different fire histories, including sites that had burned repeatedly before black spruce forests could fully recover. The absolute amount of carbon released by individual fires was broadly similar across fire-return intervals, but recently burned landscapes began with smaller remaining carbon pools and therefore lost a greater proportion of what remained. Repeated burning also consumed more legacy carbon — the carbon inherited from earlier vegetation and accumulated soils — and reduced the likelihood that some sites would regenerate as black spruce forest. Fire was doing more than releasing carbon in the present. By interrupting regeneration, it was weakening the landscape's capacity to store carbon in the future (Walker et al., 2025).Black spruce forests are not merely tolerant of fire but have evolved with stand-replacing fire as a recurring part of their life cycle. Their cones evolved to be little seed bombs that stay in the trees and release their seeds when they burn. This helps new trees grow on the ground where they're not protected by the canopy. That strategy works when fires recur at intervals long enough for stands to mature, rebuild their seed stocks, and accumulate biomass. Historically, boreal fire-return intervals commonly ranged from about seventy to 130 years. Black spruce may require roughly fifty years to produce enough seed for self-replacement. Yet intervals of less than thirty years are becoming more common in some areas, allowing another fire to arrive before recovery is complete (Walker et al., 2025).Under those conditions, fire can push regeneration away from black spruce toward deciduous vegetation or more open landscapes. Such places may remain biologically productive, but they are no longer the same forests. They store carbon, retain moisture, shelter organisms, but also carry subsequent fires differently. The landscape may not simply return to its former state after disturbance; it may cross into another ecological regime, organized by different species, intervals, and feedback loops.MEMORY, MEANING, AND MALLEABILITYIt is tempting to call such transformations unprecedented. In some measurable respects, they are. We know Industrial greenhouse-gas emissions are rapidly altering atmospheric and ecological systems on a planetary scale, and the effects are not confined to normal oscillations around once-familiar conditions. The rate, direction, and geographic concentration of change effects forests adapted to fire and may be unable to adjust when fire's frequency exceeds the time needed for reproduction and recovery.The fact that Earth has always changed does not make the present disruption ordinary. But neither does the climate crisis mark the first time people have faced the collapse of an expected environmental order. Many humans didn't survive the Little Ice Age, but many did.For many Indigenous peoples, colonization produced generations of forced displacement, altered fire and water regimes, destroyed food systems, suppressed governance, and separated communities from ancestral lands. What dominant institutions now describe as an unprecedented disruption may appear within Indigenous histories as another transformation imposed by powers that have long treated land, water, plants, animals, and people as resources to be reorganized.The word crisis can therefore describe an observable material condition while concealing a historical one. The flames are real, but so are the questions of who altered the landscape, whose losses are treated as new, who is expected to adapt, and who gets to define recovery. Potawatomi scholar Kyle Whyte challenges this framing, arguing that a dominant “epistemology of crisis” treats environmental disruptions as radically new, imminent threats (Whyte, 2021). This framing isolates the crisis from historical contexts of colonialism, allowing institutions to justify urgent, top-down actions that bypass local consent and justice under the guise of emergency.That framing carries two recurring assumptions. The first is unprecedentedness — the belief that the past contains few usable precedents or lessons for present conditions. The second is urgency — the belief that immediate action may justify setting aside ordinary concerns about consent, justice, and responsibility. Whyte's intervention exposes how crisis language can obscure Indigenous histories of displacement and adaptation while allowing new forms of dispossession to proceed in the name of emergency responses (Whyte, 2021).Whyte is not arguing that climate change is unreal or that rapid action is unnecessary. He is asking what calls for urgency and emergency action leads people to overlook or forget.When the present is imagined as unprecedented, earlier crises become difficult to see. Climate-related relocation may be narrated as a novel problem even though Indigenous nations have extensive experience with forced removal, shrinking territories, flooding, and government-directed resettlement. Declaring the current moment historically unique can erase not only previous violence but also knowledge formed through surviving it.Whyte contrasts crisis epistemology with an “epistemology of coordination.” Coordination begins not with novelty but with constant change. It asks whether the relationships needed to respond remain intact. Those relationships take the form of kinship and mutual responsibilities, including care, consent, and reciprocity. They generate what Whyte calls the “responsible capacity to respond to constant change” (2021, p. 52).This is a big geophilosophical change in how we think about the world. Adaptation isn't just about an organism or group of organisms changing to fit their environment, it's also about how things are connected, like people, places, history, and processes (the primary focus of Interplace). Fire isn't just about heat progressively burning through plants. It depends on things like how old the trees are, how much moisture there is in the soil, how many seeds are available, if there have been any fires before, what the weather is like, and the rules and laws that people have made about how to handle fire. What the forest looks like after a fire depends on which relationships are still around.The same is true of human communities. Memory is not merely a record of what happened. It is part of the infrastructure of adaptation. It carries knowledge of earlier disturbances, durable practices, failed interventions, and obligations extending beyond the present generation. A society that repeatedly labels each disruption unprecedented may collect and reason over immense quantities of data while remaining unable — or unwilling — to learn from other histories.CELLS, CUES, AND CONTINGENCYThe adaptive value of memory may reach far deeper into life than culture or nervous systems. Evolutionary biologists Maor Knafo, Elena Casacuberta, and Iñaki Ruiz-Trillo begin a recent study with the observation that “one of life's most remarkable features is its persistent and adaptive resilience in the face of constant environmental fluctuations” (2026, p. 1). They investigated whether a single-celled organism could use an environmental cue to anticipate future stress rather than responding only after that stress arrived.Cells exposed to a predictable light and vibration cue before heat stress experienced a 12 percent mortality rate, compared to a 27 percent mortality rate for cells subjected to unpredictable, random cues. The cells didn't just adapt to the heat; they learned to anticipate it, demonstrating that even single-celled life relies on temporal regularities to survive.The authors paired this experiment with a computational model. A nonlearning “blind” agent could adapt only through genetic mutation and selection across generations. A learning agent could also revise its phenotypic strategy within its lifetime, exploring alternatives when earlier responses performed poorly. In predictable environments, learning agents achieved higher fitness because they could use environmental cues to prepare for approaching conditions.The study does not demonstrate that cells reason as humans do. Nor does a cellular experiment prove a general philosophy of life. It does, however, offer evidence that even single cells can exploit temporal regularities in their surroundings. Adaptation is not always a passive process through which an external environment selects among fixed organisms. Organisms detect, respond to, and sometimes anticipate the worlds they inhabit.The model also showed that flexibility has its limits. As the simulated environment became more unpredictable, the benefits of learning started to fade. After a certain point, the cues that used to predict what would happen next didn't work as well. Keeping things flexible came with a cost, because it didn't give them any clear guidance. In the end, simpler, fixed strategies performed better. When the environment suddenly changed, the learning agents first took a big hit because their expectations had become useless. But their flexibility eventually helped them bounce back, but it also meant they made mistakes, got confused, and took time to adjust. (Knafo et al., 2026).Let's not get carried away with the comparisons. A forest isn't a Bayesian agent, and Indigenous knowledge can't be boiled down to simple conditioning. They're different forms of life, knowledge, and organization. What they do have in common is this shared principle: adaptation relies on meaningful patterns connecting past experiences to future situations.A black spruce forest can recover from a fire when trees have time to grow and produce seeds. A cell can prepare for heat when it anticipates it. A community can adapt when things change, remembering past actions, taking responsibility, and maintaining relationships. Resilience arises from a symbiotic relationship between living things and their environment. Life's resilience isn't innate. It's more that it stems from memory, prediction, adaptation, and readiness…until the world's race surpasses its ability to keep pace.RELATIONSHIPS, RECOVERY, AND RESPONSIBILITYGeographer Karen Bickerstaff warns against the dominance of a “catastrophic gaze” that portrays climate change as an imminent, universal threat (Bickerstaff, 2026). While planetary measurements are indispensable, this abstract framing can paralyze political agency, reducing people to passive spectators awaiting either inevitable collapse or a far-off technological rescue (Bickerstaff, 2026). Although our atmosphere is shared, climate exposure, responsibility, and adaptive capacity remain radically uneven. The crisis is planetary in cause, but it is lived and experienced through particular bodies, infrastructures, and local ecosystems.This planetary abstraction often fosters a form of “cruel optimism” — a reliance on grand technological promises like geoengineering or carbon-removal systems that allow us to avoid changing our politics or holding powerful actors accountable (Bickerstaff, 2026). The dangers of these universal, top-down approaches are highly visible in fire governance. Simple, blanket policies — such as total fire suppression or restrictive carbon-offset projects — frequently ignore the diverse ecological histories of fire and displace Indigenous burning practices, which ultimately increases the flammability of the landscape.In contrast to these universalizing fixes, true responsibility must preserve and restore the unique capacities of particular systems to respond to change. This is where Kyle Whyte's epistemology of coordination can turn to practice. His push is for climate action to strengthen the relationships — such as consent, reciprocity, and intergenerational obligations. This is what is his ancestors practiced surviving constant change (Whyte, 2021).Instead of top-down emergency responses that make justice disposable in the name of speed, we need “situated action” (Bickerstaff, 2026). While individuals cannot act on a planetary scale, they can act meaningfully within their own watersheds, neighborhoods, and local political coalitions. Climate action becomes durable not when it chases abstract global targets, but when it visibly improves local health, restores specific ecosystems, and corrects immediate injustices (Bickerstaff, 2026).This is not an argument for delay. Emissions do need to fall (leading to innumerable benefits), but urgency cannot excuse us from asking what kind of world our interventions produce. The task is to connect scales without allowing the global to erase the particular. A mature black spruce indeed stores carbon, but it also stores decades of growth, fungal relationships, and a history of fire and recovery. When fire returns too soon, the forest loses not just biomass, but time. Climate responsibility begins here. Not in the fantasy of holding a restless Earth still, but in preserving the intervals, relationships, and possibilities through which living systems can continue to adapt.REFERENCESBickerstaff, K. (2026). The perils of climate catastrophism: A call to situate crisis and change. WIREs Climate Change.Knafo, M., Casacuberta, E., & Ruiz-Trillo, I. (2026). Beyond diffusion: Bayesian learning strategies in single-cell life. Walker, X. J., Mack, M. C., Black, B., Dean, J., Kemper, L. F., Potter, S., Rogers, B. M., & Truettner, C. M. (2025). Increasing wildfire frequency decreases carbon storage and leads to regeneration failure in Alaskan boreal forests. Fire Ecology.Whyte, K. (2021). Against crisis epistemology. In B. Hokowhitu, A. Moreton-Robinson, L. Tuhiwai-Smith, C. Andersen, & S. Larkin (Eds.), Routledge handbook of critical Indigenous studies (pp. 52–64). Routledge. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit interplace.io

We've spent a long time bashing Bayesian epistemology on the podcast. Unfortunately, this is sometimes taken to mean that we bash all things Bayesian. But our God-fearing listeners will be pleased to learn that we do not bash all things that the Reverend Thomas Bayes stood for. Bayesian statistics, when appropriately separated from epistemology, has many things to teach us. And instead of taking our word for it, we're bringing on an expert in this field: Alex Andorra, a research scientist at Meta and host of the Learning Bayesian Statistics podcast. The Gods Smile. Check out Alex's website and the Learning Bayesian statistics podcast. We discuss 538 and election forecasting Sports analytics and why Messi is objectively the best What is Bayesian statistics? What is the difference between Bayesian and frequentist statistics? What is the allure of Bayesian statistics? Are priors best thought of as beliefs? How do critical rationalists make decisions? References Learning Bayesian Statistics: https://learnbayesstats.com/ Silver Bulletin: https://www.natesilver.net/ Vaden's blog post on decision making: https://vmasrani.github.io/blog/2020/vaden_second_response/#what-other-options-to-decision-theory-are-there # Socials Follow us on Twitter at @alex_andorra, @IncrementsPod, @BennyChugg, @VadenMasrani Come join our discord server! DM us on twitter or send us an email to get a supersecret link Become a patreon subscriber here. Or give us one-time cash donations to help cover our lack of cash donations here. Click dem like buttons on youtube Send us an email because you have - no, because you want to - over at incrementspodcast@gmail.com.Special Guest: Alex Andorra.

Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome workTakeaways:Q: What is Variational Bayesian Monte Carlo (VBMC) and how is it different from Bayesian optimization?A: VBMC borrows the machinery of Bayesian optimization but aims at a different target. Bayesian optimization fits a Gaussian process surrogate to an expensive function and uses it to hunt for the optimum. VBMC instead treats the log-posterior as the function to model, evaluates it at a few carefully chosen points, and keeps the whole reconstructed shape rather than just its peak. That gives you the full posterior, not a single best-fit value. Where MCMC might need tens of thousands to millions of evaluations, VBMC often reconstructs a good posterior approximation from a few hundred, which matters when each evaluation is slow.Q: When should you reach for PyVBMC, and when is it the wrong tool?A: Two symptoms tell you PyVBMC might help. First, speed: if a single evaluation of your log density takes on the order of a second, running MCMC over tens of thousands of evaluations becomes painful, and PyVBMC's few-hundred-evaluation budget pays off. Second, dimensionality: because it leans on a Gaussian process surrogate, it works well up to roughly 10 to 15 parameters and degrades beyond that. If your model already runs fine in Stan or PyMC, you do not need it. It shines for expensive, low-dimensional models common in science and engineering, where you are modeling a process rather than composing nice distributions.Full takeaways hereChapters:00:18:13 What is Variational Bayesian Monte Carlo (VBMC) and how does it differ from Bayesian optimization?00:30:21 When should you use VBMC versus BADS in practice?00:31:20 What is Bayesian Adaptive Direct Search (BADS) and how does its hybrid optimization strategy work?00:39:18 What are neural processes, and why are transformers a natural neural process architecture?00:45:54 What is the Amortized Conditioning Engine (ACE) and what problem does it unify?00:55:42 What do PriorGuide and the new autoregressive buffer paper solve for amortized inference?01:02:03 How does the new autoregressive buffer speed up predictions in transformer probabilistic models?01:06:11 What is Luigi Acerbi's vision for a foundation model for inference?01:09:26 What is ALINE and how does it add active data acquisition to amortized inference?01:12:43 How does Luigi Acerbi connect LLM agents, Bayesian decision theory, and the nature of intelligence?01:18:44 For a PyMC, Stan, or NumPyro user, where should you start with VBMC, BADS, or BayesFlow?Thank you to my Patrons for making this episode possible!Links from the show here

Sam Harris speaks with Siddhartha Mukherjee about the science of cancer. They discuss the updated edition of The Emperor of All Maladies, whether cancer is one disease or many, why prevention is so hard, inflammation and air pollution as carcinogens, the myth that cell phones cause cancer, liquid biopsies and Bayesian reasoning, immunotherapy and CAR T cells, drug pricing, the promise of AI in drug discovery, the state of American medical science, and other topics. If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe.

Send us Fan MailIn this Journal Club, Ben and Daphna dig into two new papers on PDA management in our smallest patients. First, the SMART-PDA pilot RCT from Souvik Mitra and colleagues, which uses comprehensive hemodynamic screening to selectively treat high-volume shunts in infants born before 26 weeks, and whose striking Bayesian signal for reduced pulmonary hemorrhage and NEC stopped the trial early. Then a companion JAMA Network Open comparative effectiveness study across four pharmacotherapy regimens. Along the way, Ben shares hemodynamics pearls from his Montreal training: why left ventricular output, LA:Ao ratio, and transductal velocity matter more than PDA diameter alone.----Selective early medical treatment of the patent ductus arteriosus in extremely low gestational age infants: a pilot randomised controlled trial (SMART-PDA). Mitra S, Hebert A, Castaldo MP, Disher T, El-Naggar W, Dhillon S, Alhassen Z, Koo J, Katheria AC, Hyderi A, Kumaran K, Ting J, Surak A, Larocque J, Pepper D, Hornberger L, Makoni M, Weisz DE, Jain A, Bacchini F, Cameron-Nola AJJ, Hatfield T, Dorling J, McNamara PJ, Thabane L.Arch Dis Child Fetal Neonatal Ed. 2026 May 18:fetalneonatal-2026-330462. doi: 10.1136/archdischild-2026-330462. Online ahead of print.PMID: 42150872Pharmacologic Therapies for Patent Ductus Arteriosus in Extremely Preterm Infants. Mitra S, Jain A, Ting JY, Ben Fadel N, Drolet C, Abou Mehrem A, Soraisham AS, Jasani B, Louis D, Lapointe A, Dorling J, Khurshid F, Hyderi A, Kumaran K, Toye J, Harabor A, Weisz DE, Stavel M, Morin A, Bhattacharya S, Lalitha R, Afifi J, Augustine S, Castaldo MP, Hatfield T, Su YC, Shah PS; Canadian Neonatal Network Investigators.JAMA Netw Open. 2026 Jun 1;9(6):e2617477. doi: 10.1001/jamanetworkopen.2026.17477.PMID: 42262753 Free PMC article.Support the showAs always, feel free to send us questions, comments, or suggestions to our email: nicupodcast@gmail.com. You can also contact the show through Instagram or Twitter, @nicupodcast. Or contact Ben and Daphna directly via their Twitter profiles: @drnicu and @doctordaphnamd. The papers discussed in today's episode are listed and timestamped on the webpage linked below.Enjoy!

In this episode, Professor Paola Cinnella - Professor of Fluid Mechanics at Sorbonne University and Director of the Sorbonne Cluster for Artificial Intelligence (SCAI) - joins Neil to discuss her path from classical fluid mechanics and high-order numerical methods into uncertainty quantification, Bayesian methods, data-driven turbulence modeling and AI for Science.Paola has built a career at the intersection of CFD, compressible and turbulent flows, dense gas dynamics, uncertainty quantification, robust optimization and machine learning. We discuss academic careers, dense gases, RANS uncertainty, AirfRANS, surrogate modeling, scientific publishing, education in the age of AI, and the idea of the "centaur scientist".Key topicsFluid mechanics, CFD and high-order schemesDense gases, real-gas effects and expansion shockwavesUncertainty quantification and Bayesian methodsRANS turbulence-model uncertaintyAirfRANS and CFD datasets for machine learningTurbulence modeling vs surrogate modelingScientific publishing and ML-for-CFD standardsSCAI and AI for ScienceEducation, ChatGPT and centaur scientistsPapersQuantification of model uncertainty in RANS simulations: A review - Heng Xiao, Paola Cinnellahttps://doi.org/10.1016/j.paerosci.2018.10.001Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression - Martin Schmelzer, Richard P. Dwight, Paola Cinnellahttps://doi.org/10.1007/s10494-019-00089-xBayesian estimates of parameter variability in the k-epsilon turbulence model - W.N. Edeling, P. Cinnella, R.P. Dwight, H. Bijlhttps://doi.org/10.1016/j.jcp.2013.10.027AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutionshttps://arxiv.org/abs/2212.07564Data-driven turbulence modeling - Paola Cinnellahttps://arxiv.org/abs/2404.09074Direct numerical simulations of supersonic turbulent channel flows of dense gases - Luca Sciacovelli, Paola Cinnella, Xavier Gloerfelthttps://doi.org/10.1017/jfm.2017.237LinksPaola Cinnella named Director of SCAIhttps://scai.sorbonne-universite.fr/news/paola-cinnella-new-directorSCAIhttps://scai.sorbonne-universite.fr/Paola Cinnella - HAL publicationshttps://cv.hal.science/paola-cinnellaPaola Cinnella - Google Scholarhttps://scholar.google.com/citations?hl=fr&user=wBRA0JAAAAAJERCOFTAC SIG 54 - Machine Learning for Fluid Dynamicshttps://www.ercoftac.org/special_interest_groups/54-machine-learning-for-fluid-dynamics/master-of-science-internships/Chapters00:00 Podcast intro00:39 Introducing Prof. Paola Cinnella03:28 Conversation begins03:56 How Paola found fluid mechanics07:09 Moving from Italy to France08:37 High-order schemes and compressible flows09:30 Building an academic career12:06 Dense gases and uncertainty quantification15:16 Expansion shockwaves and real-gas effects19:17 Returning to Paris and academic mobility24:52 Academia, passion and persistence27:51 Bayesian methods and turbulence uncertainty30:47 Learning statistics across disciplines33:07 LearnFluidS, AirfRANS and CFD datasets36:33 Skepticism and physics in ML turbulence modeling40:41 Could ML lead to a universal turbulence model?42:59 Turbulence models, surrogate models and RANS45:03 Why LES alone cannot solve optimization47:15 Multi-fidelity modeling49:08 What Computers & Fluids looks for in ML-for-CFD papers54:05 CFD metrics vs machine-learning metrics57:13 Overselling, publication pressure and quality62:22 SCAI and AI for Science66:07 Cross-disciplinary AI for Science69:26 Education in the AI era72:44 Critical thinking and AI outputs78:15 AI as a companion, not a replacement81:42 AlphaFold and the future of discovery83:43 Training centaur scientists85:11 Closing thoughts

Aaron Brown is an author and risk management professional, formerly the Chief Risk Officer at the hedge fund AQR. Aaron's recent works are titled Wrong Number: How to Extract Truth From a Blizzard of Quantitative Disinformation and The Poker Face of Wall Street. Greg and Aaron discuss why quantitatively flawed studies still persist today. Aaron argues that the central problem is not just incompetence or conspiracy but a macro phenomenon he calls tribalism, combined with the diffusion of responsibility across authors, reviewers, journals, and journalists. He discusses examples, including an NTSB “Chinatown bus” study, a USAID mortality claim, a Chunnel fire-risk study, an observational marijuana/heart-attack paper, and a study claiming 40% of COVID deaths were caused by evictions and later cited in courts and legislation. They contrast academia's weak incentives with finance and gambling, where betting forces accountability, and Aaron describes the empirical Bayesian approach he prefers using base rates and evidence. *unSILOed Podcast is produced by University FM.* Episode Quotes: There are consequences to publicizing bad research. [34:45] I think most researchers are careful not to let the university press office get ahold of their bad study. They're careful not to go out and give interviews on it. The ones who forget that, they're the ones who cause the problems and get caught. I mean, not many people do get caught, and the consequences of getting caught are pretty low, but it can happen. You lose professional credibility, and that's extremely important, you know? That's really the be all and end all for most researchers I know, is what their peer researchers think of them. And that's where you get hurt. In fact, you get hurt even for getting publicity for your good work, you know? There still is a real feeling in a lot of sciences that the guy in the headline is not a real scientist. Why are people so easily misled by quantitative information? [05:32]  It's been documented over and over in lots of different ways, that most published research findings are false, and yet nobody seems to care.  Aaron discusses the promise and pitfalls of Bayesian reasoning.  [57:28] You don't have to go all the way to Bayesian to know that what they're doing in the journals is wrong. The journal, the frequentist, the Fisher classical hypothesis testing, the gold standard, double-blind control trials—those things are just wrong. And you don't have to go all the way to Bayesianism. You can just say, "Okay, we can just show mathematically that those don't work." Show Links: Recommended Resources: National Transportation Safety Board (NTSB) Tribalism United States Agency for International Development (USAID) Evaluating the impact of two decades of USAID - Lancet Study Channel Tunnel Fires Ronald Fisher Intergovernmental Panel on Climate Change (IPCC) Outlive: The Science and Art of Longevity National Bureau of Economic Research (NBER) Francesca Gino Robin M. Hogarth Harrison White Bayesian Statistics UnSILOed 584: David Zweig - Examining School Closure Policies During the Pandemic Guest Profile: LinkedIn Profile Reason Profile Wikipedia Profile Guest Work: Amazon Author Page Wrong Number: How to Extract Truth From a Blizzard of Quantitative Disinformation Red-Blooded Risk: The Secret History of Wall Street The Poker Face of Wall Street Financial Risk Management For Dummies Fischer Black and the Revolutionary Idea of Finance A World of Chance: Betting on Religion, Games, Wall Street Wrong Number with Aaron Brown YouTube Series Google Scholar Page Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What if a materials lab on Earth could screen a hundred alloys a day with little input from the scientist? Taylor and Andrew sit down with Joseph Krause, CEO and co-founder of Radical AI, to dig into what it takes to build a self-driving lab and why most of the field is still missing the hard part. From discovering that flagship SEM and XRD instruments ship with no real data access (and rebuilding their entire OS around the workaround), to MATRIX — their multimodal vision-language model that hones in on a target property in roughly 20 experiments — Joseph walks through the technical bets that got them here. He explains why they're using language-model embeddings to teach Bayesian optimization what "28% titanium" actually means, why "scientific intuition" has to be measured as a delta between human and AI annotations, and why Radical is going all the way to manufacturing instead of licensing compositions — because the real IP, and the only training data that matters, lives on the production floor. Check out Radical AI here [LINK] This episode of the Materialism Podcast is sponsored by Momentum Transfer. Visit their website for more details about their measurement services. [LINK] The Materialism Podcast is sponsored by Materials Today, an Elsevier community dedicated to the creation and sharing of materials science knowledge and experience through their peer-reviewed journals, academic conferences, educational webinars, and more. [LINK] Thanks to Kolobyte and Alphabot for letting us use their music in the show! If you have questions or feedback please send us emails at materialism.podcast@gmail.com or connect with us on social media: Instagram, Twitter. Materialism Team: Taylor Sparks, Andrew Falkowski, & Jared Duffy.

Can you even negotiate fraud? This episode on Fraudish we discuss how negotiation and fraud are more related than you would think. I am joined by Martin Medeiros about fraud, negotiation, and AI. Martin is an attorney (I know, another one!) who helps organizations build value by treating intellectual property as a strategic asset.In the episode, Martin describes his career building Fortune 500 tech negotiation teams. We discuss his data-driven negotiation approach using game theory and Bayesian updating. And of course, we discuss using LinkedIn as attorney marketing. Connect with Martin: https://www.linkedin.com/in/martin-medeiros2/Persuasion Lab: https://thepersuasionlab.com/Buckley Law: https://www.buckley-law.com/martin-medeiros/

Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome workTakeaways:Q: What's the difference between Bayesian statistics and Bayesian epistemology?A: Bayesian statistics uses Bayes' theorem on actual data: you put a prior over parameters, combine it with a likelihood, and the data is allowed to tell you your model is wrong. Vaden loves it. Bayesian epistemology, in his tongue-in-cheek phrase, is "Bayesian statistics minus the statistics" - taking Bayes' theorem as a general account of how anyone should reason under uncertainty, including about events where there is nothing to count. The first is falsifiable and grounded; the second, he argues, lets people attach authoritative-sounding numbers to pure belief.Q: Why is it a problem to put a probability on a one-off future event like human extinction?A: Because there are no statistics behind it. Vaden's trigger example is Toby Ord's The Precipice, where a data-derived probability (supervolcanoes per millennium) is placed side by side with a probability of extinction-by-superintelligence that came from no data at all. His reaction is the statistician's first instinct: where are the numbers coming from, and what could ever make them come out differently? A subjective degree of belief is fine as a hunch. The trouble starts when it is communicated as though it were an objective, data-grounded frequency.Q: What does Vaden Masrani actually like about Bayesian statistics?A: The freedom to encode domain knowledge as a prior and have the result respect common sense - estimating an average human height, you can rule out zero and a hundred feet before seeing a single measurement. But the part he keeps stressing is falsifiability: you fit the model, compare it to data, and the data can tell you the model was bad. That contact with reality is exactly what makes the statistics legitimate and what the epistemology lacks. On Bayesian-versus-frequentist for engineering problems, he says he has no dog in the fight -- both are useful, and any working statistician uses both.Full takeaways hereChapters:00:24:01 What's the difference between Bayesian statistics and Bayesian epistemology?00:33:12 How can Bayesian epistemology lead to bad real-world decisions?00:36:36 Is Bayesian or frequentist statistics better for real-world problems?00:39:31 What is the problem of induction, and how does Bayesian epistemology try to solve it?00:43:50 What are the main logical problems with Bayesian epistemology?00:48:40 What is Popper's critical rationalism, and how does falsifiability fit in?00:52:31 How does critical rationalism work when you can't run a clean experiment?01:15:03 Why should you treat criticism as a gift, even when it hurts?01:19:54 How do Stoicism and equanimity help you handle criticism?01:23:19 Why does critical rationalism apply to everyday life, not just science?Thank you to my Patrons for making this episode possible!Links from the show here

Thomas Ahle wants Normal Computing to be the Lovable for chip design: type your intent, and a swarm of agents carries it from design through optimisation, formalisation and verification to tape-out. To get there, his team at wrote their own open-source Verilog simulator, 580,000 lines in 43 days, because commercial EDA verifiers run about $10,000 per core and there are no decent open-source compilers to build on.That sets up the question Tim keeps pressing: if an agent can produce a chip design, a proof, or a working program, how do you actually know it is correct? Passing 70% of tests is not the same as being right, and a single fabricated bug can cost a company a fortune. They dig into ProgramBench (rebuild a program from its tests, roughly 0% success), the difference between structure and competence, and the "understanding debt" you take on when nobody reads the code.From there: auto-formalisation in Lean and the AlphaProof trick of training on prove-or-disprove; why there is no single true representation of a spec (Petri nets, TLA+, Erik Curiel's "math does not represent"); and thermodynamic computing, where Normal Computing's CN101 chip is built so that its physical noise *is* the computation, settling a stochastic differential equation in hardware to invert a matrix. Plus Bayesian uncertainty, specialisation, the Chomsky hierarchy, AI slop, and whether performance is all that matters.Recorded in Zurich.Disclosure: Normal Computing paid our production and travel costs for this show. We retained full editorial control. They did not see the video before publication, and we did not show it to them or discuss it with them beforehand.---TIMESTAMPS:00:00:00 Meet Thomas Ahle: the Lovable for chip design00:03:41 Why hardware needs formal verification00:06:36 Ten thousand dollars per core and a six-month agent run00:07:40 Rebuilding programs from tests: ProgramBench and zero percent00:12:15 Structure vs competence: can you learn a program from behavior?00:15:27 Continual learning, abstraction, and Claude as an ecosystem00:23:17 Autoformalization and the AlphaProof trick00:29:31 No single true representation: specs, Petri nets and TLA+00:34:43 Thermodynamic computing: when noise is the computation00:37:32 Bayesian uncertainty in the age of token streams00:41:12 Hybrid compute: vibe-coding loops, binaries and Stockfish00:44:44 Co-design, central-AI apps and API pricing00:49:45 Chain of thoughtlessness and the Chomsky hierarchy00:53:40 AI psychosis, slop and the broken social contract00:57:34 Typing it yourself, teamwork and performance vs competence---REFERENCES:person:[00:00:10] Thomas Ahlehttps://thomasahle.comorganization:[00:00:27] Normal Computinghttps://normalcomputing.com/paper:[00:11:21] ProgramBench: Can Language Models Rebuild Programs From Scratch?https://arxiv.org/abs/2605.03546[00:31:55] Autoformalizing Memory Device Specifications with Agentshttps://arxiv.org/abs/2605.00058[00:35:20] Thermo AI and the Fluctuation Frontierhttps://arxiv.org/abs/2302.06584[00:36:40] Thermo Comp System for AI Applicationshttps://arxiv.org/abs/2312.04836[00:37:05] Thermodynamic Linear Algebrahttps://arxiv.org/abs/2308.05660[00:44:50] An efficient probabilistic hardware architecture for diffusion-like modelshttps://arxiv.org/abs/2510.23972tool:other:[00:01:00] Building an Open-Source Verilog Simulator with AI: 580K Lines in 43 Dayshttps://normalcomputing.com/blog/building-an-open-source-verilog-simulator-with-ai-580k-lines-in-43-days[00:02:55] Normal Computing Announces Tape-Out of the World's First Thermodynamic Computing Chip (CN101)https://www.normalcomputing.com/blog/normal-computing-announces-tape-out-of-worlds-first-thermodynamic-computing-chip[00:32:02] DRAMBench: Autoformalizing DRAM Specifications with Timed Petri Netshttps://www.iese.fraunhofer.de/blog/drambench-autoformalizing-dram-specifications/---ReScript: https://app.rescript.info/share/ff9684a112ab37744096adaeb097a263

Would You Hire Your Own Graduate? What if the true measure of a university wasn't its rankings, campus amenities, or brand recognition—but the quality of the young adults it sends into the world?In this episode of CEO Blindspots®, UATX⁠ President ⁠Dr. Carlos Carvalho discusses a bold question facing higher education today:Are colleges producing graduates who are truly prepared for life, leadership, and work?Dr. Carvalho shares how UATX is challenging conventional assumptions about higher education through merit-based admissions, rigorous intellectual inquiry, open debate, and meaningful real-world experience. We explore why employers increasingly seek graduates who can think critically, communicate effectively, navigate disagreement, and contribute from day one.Whether you're an employer searching for future leaders, a parent evaluating college options, or a student determined to make the most of your education, this conversation offers a fresh perspective on what higher education can be designed to achieve.CEO Blindspots® Podcast Guest: ⁠Dr. Carlos Carvalho⁠Dr. Carlos Carvalho is the President of the ⁠University of Austin⁠. Prior to taking on this role, he spent 15 years as a professor at the University of Texas at Austin's McCombs School of Business, where he held the La Quinta Centennial Professorship and founded the Salem Center for Policy. A native of Brazil, Dr. Carvalho earned his doctorate in statistics from Duke University and has also taught at the University of Chicago Booth School of Business. His research focuses on Bayesian statistics in complex, high-dimensional problems with applications ranging from economics to genetics to public policy. At ⁠UATX⁠, he is leading a bold effort to build a new university that stands for American principles and academic excellence.

    Few careers in military medicine trace an arc as wide as that of CAPT (Ret) Kimberly Elenberg, DNP, RN. In this episode she sits down with WarDocs to map a journey that began as an ROTC cadet who joined because she saw students rappelling down a building in Philadelphia, and that has since carried her from the bedside at Walter Reed Army Medical Center to the role of principal investigator on a Carnegie Mellon University team competing in the DARPA Triage Challenge. Along the way she changed uniforms, disciplines, and altitudes of responsibility, but never lost the thread that ties it all together: people first, and the relationships that make hard things possible.     CAPT (Ret) Elenberg describes how early mentors shaped her. Colonel Graham showed her that putting people first is a practice, not a slogan. Major McGee backed her instinct for innovation, and as a young nurse on Ward 51 she built one of the first patient education centers in a military treatment facility, learned to set up networks and hardware, and pursued nursing informatics before the field was common. She recounts moving to research at NIH, where her work on TPA for clearing central line catheters was later adopted as best clinical practice, and her decision to volunteer as an EMT and medic so she would understand field medicine as well as hospital medicine.       From there the conversation follows her into the U.S. Public Health Service, where after 9/11 the Surgeon General asked her to help build the nation's deployable response teams from concept to operation, training them in real communities facing real crises. She explains how anthrax and zoonotic disease drew public health into agriculture and food security, how her long relationship with Carnegie Mellon's Auton Lab began with a bus trip and a phone call, and how that mathematical grounding in probabilistic modeling resurfaced when she was asked to model the effects of policy during COVID and, later, to track military security assistance flowing to Ukraine.     The episode closes on the present and the future: autonomous triage payloads that can read a casualty's physiological state without touching them, robotic snakes that might pack non-compressible hemorrhage, swarms of drones and ground robots that find the wounded and feed the right information to the right echelon. Throughout, CAPT (Ret) Elenberg returns to her core lessons — trust your chain of command, define what success really looks like, build on small wins, and never limit yourself to your military occupational specialty. From an orphanage and a food-service background to teaching at the National Defense University, hers is a story about doors held open and relationships that endure. Chapters (00:54-07:11) From Rappelling Cadet to Innovating Army Nurse (07:11-16:48) Building the Nation's Public Health Response Teams (16:48-22:24) Biosurveillance Modeling COVID and Ukraine Aid (22:24-32:32) The Power of Relationships Across a Career (32:32-37:37) Autonomy Confidence and Knowing When to Explore (37:37-51:33) The DARPA Triage Challenge and Lessons That Last Chapter Summaries (00:54-07:11) From Rappelling Cadet to Innovating Army Nurse The guest traces her start as an ROTC cadet drawn in by students rappelling down a Philadelphia building, her commissioning as an Army nurse, and her first duty station at Walter Reed Army Medical Center. Early mentors, including Colonel Graham and Major McGee, taught her that people truly come first and backed her instinct for innovation. On Ward 51 she built one of the first patient education centers in a military treatment facility while teaching herself websites, networking, and nursing informatics.   (07:11-16:48) Building the Nation's Public Health Response Teams Her NIH research on TPA for central line catheters was later adopted as best clinical practice, and she volunteered as an EMT and medic to learn field medicine. After moving to the U.S. Public Health Service for family stability, she answered the Surgeon General's call following 9/11 to build the nation's deployable response teams from concept to operation. Anthrax and zoonotic disease pulled public health into agriculture and food security across the federal enterprise.   (16:48-22:24) Biosurveillance Modeling COVID and Ukraine Aid Tasked to advise on detecting events and discerning intent, she leaned into probabilistic modeling and a long relationship with Carnegie Mellon's Auton Lab that began with a bus trip and a phone call. As Director of Population Health at the Defense Health Agency she modeled total force fitness, then was asked to model the effects of policy during COVID rather than the disease itself. The work forced coordination across agencies, departments, and services on a scale not seen since World War II.   (22:24-32:32) The Power of Relationships Across a Career Describing herself as an introvert, she explains why relationships are the engine of accomplishment, recalling a Ranger literally pushing her up a mountain during advanced camp after a car accident. Those bonds endured and resurfaced decades later in Texas during the DARPA Triage work. She recounts retiring out of Poland after 28 years, where she stood up a secure network to coordinate 26 non-doctrinal partners supporting aid to Ukraine.   (32:32-37:37) Autonomy Confidence and Knowing When to Explore She makes the case for military service as a path to clinical autonomy and the chance to think, decide, and do research that civilian roles often do not allow. She reflects on how to know when to pursue a new opportunity: trust your chain of command, negotiate and listen when you are the one in charge, and act on principles of doing no harm. Confidence, she says, means not being afraid to fail.   (37:37-51:33) The DARPA Triage Challenge and Lessons That Last She gives a plain-language tour of her team's autonomous triage work — payloads that read physiological state without touching a casualty, visual reasoning models tempered by Bayesian rigor, and platforms that deliver the right information to each echelon. Using a DoD-wide tobacco policy as a case study, she explains the art of the doable and building success on small wins. She closes with advice on confidence, integrity, and holding doors open for the next generation.   Take Home Messages Cross disciplines to scale care: The greatest gains often come from teaming up outside your own specialty. Pairing clinical insight with engineering, informatics, and operations lets a single provider extend capability and capacity far beyond what one profession can deliver alone. People first is a practice, not a slogan: Leaders who genuinely put people first earn the trust that makes hard missions possible. The example of a leader who recognized her team while facing her own serious illness shows that the principle is proven in action, not in words. Relationships are the engine of accomplishment: No one knows everything, and progress depends on the people willing to push you up the mountain. Networks built early endure for decades and can be called on when the mission needs them most. Define what success really looks like: Insisting on the perfect outcome can stall progress entirely; agreeing on the art of the doable moves the mission forward. Real success is often a series of small wins that build on one another over time. Confidence means not being afraid to fail: Growth lives outside the comfort zone, and everyone fails sometimes. Acting with honesty, integrity, and your best effort each day — then trusting tomorrow brings another chance — is what builds lasting confidence. Episode Keywords military medicine, Army nurse, military nursing, WarDocs, military medicine podcast, public health service, USPHS, DARPA Triage Challenge, autonomous triage, battlefield medicine, combat casualty care, Carnegie Mellon University, Auton Lab, nursing informatics, biosurveillance, COVID modeling, population health, Defense Health Agency, Walter Reed, military innovation, medical robotics, drone medicine, military mentorship, veteran leadership, military medical research Hashtags #MilitaryMedicine, #WarDocs, #ArmyNurse, #PublicHealth, #BattlefieldMedicine, #DARPA, #MilitaryInnovation, #VeteranLeadership   Biography Dr. Kimberly Elenberg, a retired USPHS Captain, is the Director of Data and Mission Partner Sharing at ECS. A distinguished leader in biosurveillance and emergency response, she applies data science to enhance national security. Notably, she served as the incident response commander for modeling and analytics for the Secretary of Defense COVID Task Force. Previously, as a principal scientist at Carnegie Mellon University, she advanced autonomous systems for biosurveillance. Dr. Elenberg consistently bridges theoretical research with practical healthcare delivery, leveraging her clinical expertise and military discipline to safeguard public health. Her exceptional contributions have earned her several highly prestigious awards, including the 2022 Defense Superior Service Medal, the 2022 USPHS Distinguished Service Medal, and the 2020 National Emergency Preparedness Award for her outstanding operational acumen.         Honoring the Legacy and Preserving the History of Military Medicine The WarDocs Mission- WarDocs exists to honor the legacy of Military Medicine, preserve its history, and inspire every generation — across all Services, Corps, and Ranks — to serve with excellence and pride. Through mentorship, coaching, and education, we equip those considering, entering, and serving in military medicine with the knowledge, connections, and community they need to thrive. We celebrate Who we are, What we do, and, most importantly, How we serve Our Patients, the DoW, and Our Nation.   Find out more and join Team WarDocs at https://www.wardocspodcast.com/ Check our list of previous guest episodes at https://www.wardocspodcast.com/our-guests Subscribe and Like our Videos on our YouTube Channel: https://www.youtube.com/@wardocspodcast Listen to the “What We Are For” Episode 47. https://bit.ly/3r87Afm   WarDocs- The Military Medicine Podcast is a Non-Profit, Tax-exempt-501(c)(3) Veteran Run Organization run by volunteers. All donations are tax-deductible and go to honoring and preserving the history, experiences, successes, and lessons learned in Military Medicine. A tax receipt will be sent to you. WARDOCS documents the experiences, contributions, and innovations of all military medicine Services, ranks, and Corps who are affectionately called "Docs" as a sign of respect, trust, and confidence on and off the battlefield, demonstrating dedication to the medical care of fellow comrades in arms.     Follow Us on Social Media Twitter: @wardocspodcast Facebook: WarDocs Podcast Instagram: @wardocspodcast LinkedIn: WarDocs-The Military Medicine Podcast YouTube Channel: https://www.youtube.com/@wardocspodcast    

This week, we dive deep into the world of data, decision-making, and uncertainty with Dale Nesbitt, a lecturer at Stanford and principal at Arrowhead Economics. Drawing on his unique upbringing in a mining town, Dale Nesbitt shares how witnessing raw data collection firsthand shaped his perspective on what it really takes to make informed decisions—hint: it's not just about having more data.Together, we explore the pitfalls of relying solely on data for critical choices, the importance of understanding probability and risk, and why data-gathering itself is often a noisy and imperfect process. From commodity pricing and speculation in oil markets to the real-world impact of data-driven decisions in healthcare, Dale Nesbitt reveals why true analytic power comes from combining rigorous analysis, sound judgment, and the right kind of data—not just more of it.Join us as we challenge myths around "data-driven" decisions, unpack lessons from COVID-era data science, and discover why wisdom of the crowd, probability, and a healthy respect for uncertainty are key to navigating our data-rich world.LinksDale's LinkedIn profile -https://www.linkedin.com/in/dale-nesbitt-b574a83a/Watch on YouTube -https://www.youtube.com/watch?v=USOKgv1avHoTime Stamps00:00 Growing up in a mining town05:44 Data as the New Crude Oil07:31 Estimating and Understanding Stochastic Processes12:49 Impact of Strait of Hormuz Closure14:19 Challenges of AI in Economics17:05 Betting on events and elections21:43 Bayesian analysis and hydroxychloroquine data23:28 Understanding data and judgment26:38 Analyzing data for better decisions

Today's clip is from Episode 158 featuring Stefan Radev. In this conversation, Alex Andorra and Stefan break down a core argument from their paper: Bayesian statistics has never been more computational than it is now, and simulation is the thread that ties the whole workflow together.Stefan parcellates the Bayesian workflow into four stages, and this clip covers the first two. Stage one is model specification, where the workflow community has long recommended prior predictive checks. You can do this informally, just running simulations from your model and eyeballing whether the output meets your expectations, or formally, à la Michael Betancourt, by pushing your model's high-dimensional output through a transformation into a low-dimensional, interpretable space and checking it against reality. The punchline: a surprising number of models can be discarded before you've even seen real data, yet Stefan notes these checks remain underused in practice.Stage two is model verification, where the question shifts to whether your inferences are well calibrated. This is the territory of simulation-based calibration and parameter recovery studies, classic tools that have always carried a steep computational price. You simulate thousands of synthetic datasets and run inference on every single one, which is exactly why these checks are so often skipped in papers, even though doing one well can be a contribution in its own right.Here's where amortized simulation-based inference changes the math entirely. Checks that used to take days now take seconds, and instead of laboriously running inference dataset by dataset, you get millions of posterior samples essentially for free. The calibration checks that the field has always known it should be doing finally become cheap enough to actually do.Get the full discussion hereSupport & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work

In this episode of Communicable, Emily McDonald and Josh Davis are joined by Roger Lewis (USA) and Ian Marschner (Australia) to compare and contrast Bayesian and frequentist statistical approaches. The panel discusses the fundamental principles of both methods, common misconceptions, and the extent to which they are often more similar than many realise. Together, they explore their use in clinical trial design, analysis, and reporting, including adaptive trials and sequential learning. Additional topics include sample size misconceptions, regulatory versus clinical thresholds, and the challenges of interpreting post hoc reanalyses of negative trials.This episode was edited by Kathryn Hostettler and the executive producer of Communicable is Angela Huttner.  Further reading:Berry SM, et al. Bayesian Adaptive Methods for Clinical Trials (Chapman & Hall/CRC Biostatistics Series). Boca Raton (FL): CRC Press; 2010. FDA Guidance Document: Use of Bayesian Methodology in Clinical Trials of Drug and Biological Products FDA, 2026, https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-bayesian-methodology-clinical-trials-drug-and-biological-productsLee TC, et al. Contextualizing the use of corticosteroids in severe Pneumocystis jirovecii pneumonia through a Bayesian lens. CMI Comms 2025, https://www.cmi-comms.org/article/S2950-5909(25)00082-4/fulltextLivingston EH and Lewis RJ. JAMA Guide to Statistics and Methods, https://jamaevidence.mhmedical.com/Book.aspx?bookId=2742Marschner I. Confidence distributions for treatment effects in clinical trials: Posteriors without priors. Stat Med 2024, doi: 10.1002/sim.10000.Whitehead J. The design and analysis of sequential clinical trials. Revised 2nd ed. Chichester: John Wiley & Sons; 1997.

Guests Akash Kulgod and Dr. Sanjeev Kulgod and host Dr. Davide Soldato discuss JCO article, "Canine Olfaction Combined with Bayesian Modeling for Multi Cancer Detection from Breath Samples, a Phase 2 Study in India" and the innovative breath-based canine olfaction for multi-cancer detection in low-resource settings, the Bayesian modeling integration, and future prospects for scalable, non-invasive cancer screening methods. LINK TO FULL TRANSCRIPT

Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome workTakeaways:Q: What is a Bayesian occupancy model and what problem does it solve?A: An occupancy model accounts for the fact that you don't always detect a species when surveying for it, especially when the species is rare. A naive count of where you found it underestimates true occupancy. The model adds a repeated-measures component: you visit each site multiple times, and from the pattern of detections vs. non-detections it estimates a detection probability. Matthijs framed it as a zero-inflation structure where the zero-inflation happens at the site level rather than the observation level -- which keeps the model conceptually simple, just a standard GLM with a Bernoulli “is the species here at all?” stacked on top of a detection-rate process.Q: What are Automated Recording Units and why don't traditional occupancy models handle them well?A: ARUs are camera traps and acoustic monitors that record continuously over deployment periods of days, weeks, or months. The data they produce isn't a sequence of discrete human-led surveys; it's a continuous-time observation stream. Traditional occupancy models were designed for the discrete case -- a human visits a site, records yes or no, goes home. With ARUs, the question becomes how to bin or threshold the continuous data without losing the richer signal it actually contains.Q: When should you not reach for occARU?A: When your dataset is large and your survey interval is fine-grained. The bottleneck is Stan's fitting speed -- years of daily count data across many sites will fit slowly. The workaround is to bin coarser (weekly or monthly), which doesn't hurt occupancy estimation at all and only loses some detection-rate resolution. If you're only interested in occupancy, big grouping windows are fine.Full takeaways hereChapters:00:12:14 What is an occupancy model and what problem does it solve?00:16:16 What are Automated Recording Units and why do they need different models?00:18:45 What is the occARU R package and why does it exist?00:23:55 Why does occARU model counts directly rather than binary detection?00:26:38 What does multi-species hierarchical modeling with Gaussian processes look like?00:32:22 How does occARU implement Gaussian processes efficiently?00:41:01 Why are Gaussian processes such a powerful but tricky modeling tool?00:44:11 What is variance decomposition with global-local shrinkage priors?00:49:02 How does occARU leverage recent Stan features for zero-sum constraints?00:57:37 When does within-chain parallelization actually help?01:01:30 How does Monte Carlo integration reduce high Pareto-k values?01:15:27 When does occARU underperform and what's on the roadmap?Thank you to my Patrons for making this episode possible!Links from the show here.

The Experience Strategy Podcast Hosts: Aransas Savas, Dave Norton, Joe Pine Featured articles: "Death of the Segment: Why Personas Are Killing Personalization" — SwiftERM "Your Personas Are Outdated. It's Time to Evolve Your Approach." — Audrey Chee-Read, Principal Analyst, Forrester Every other post on LinkedIn is announcing the death of something. Most of it is alarmist storytelling dressed up as insight. But under the noise, two recent articles — one from SwiftERM, one from Forrester — are pointing at a real problem: personas and segmentation, built for an earlier era of marketing, have become a drag on personalization in the era of AI. Dave, Joe, and Aransas trace where personas actually came from, why they got merged with segmentation, what AI changes about the math, and what should replace the persona as the stable determinant companies are still looking for. The answer Dave keeps returning to: situations. Key Ideas Personas were never built for marketers. Dave opens with the history. The persona originated around 1999–2001 as a design thinking technique to get engineers to think more like customers. It worked. Then it migrated into marketing and merged with segmentation, and the original purpose got lost. Segmentation is the search for a stable determinant. Companies need something they can count on to define a market — geography, demographics, lifestyle, generation. Stable determinants make markets identifiable, and identifiable markets are countable. But the stability is increasingly fictional. Customers are not stable. They want different things at different times. Joe's arc: mass market → segments → niches → markets of one → markets within one. Joe walks the progression from Henry Ford's mass market through Alfred Sloan's segments through the minivan that opened up niche thinking. Stan Davis's Future Perfect (1987) saw the path to markets of one. What comes next is the flip: multiple markets inside every customer. Joe on a business trip is a different market than Joe on a leisure trip with his wife, even though it is the same person and the same credit card. This is the situational markets argument. Dave's frame: situations can be the new stable determinant. Friday night with your wife is a context. Monday morning before work is a context. Travel in cold Chicago is a different context than travel in France. The behavior changes with the context, even when the person does not. The SwiftERM line that lands the case. "While your team is busy building a persona for Sarah, the 35-year-old yoga enthusiast, Sarah has already moved on. She isn't a persona. She's a dynamic stream of intent." She bought a yoga mat six months ago. For the last three days, her behavior shows interest in high-end supplements and weightlifting gear. The persona missed the shift. The window of intent closed before the system caught up. Bayesian thinking is the right math for this. Predictive analytics has historically used past behavior to predict future behavior — yesterday you watched a romance, so tomorrow you will too. The newer move is using context, not just history. Yesterday you watched a romance because it was Friday and you were with your wife. The probability updates with every new piece of information. AI makes this practical at scale for the first time. The Apple Watch and Netflix examples make it concrete. The latest Apple Watch update no longer just serves up the workout you did last. It serves up the workout you usually do on that day of the week. Aransas lifts Monday and Wednesday and the watch knows. Netflix recommends romance on Friday night because the pattern holds across the whole user base. Restaurants have understood this for a hundred years — they do not serve breakfast at nine at night because they read the context. Customers have the same AI you do. Joe's reminder at the end is the one that should make every CMO uneasy. Customers can now vibecode their own shopping experience. They can customize as easily as you can customize for them, and they will configure it for their own context every time. The companies that win are the ones whose offerings can flex to the customer's situation, not the ones with the most polished persona deck. A Word on "Moments" Dave makes a careful distinction at the end. Moments is the right idea, but 20 years of design thinking have loaded the term with retail-moment-one, retail-moment-two, retail-moment-three thinking — discrete and product-out, not organic and customer-out. Situations carry the meaning without the baggage. Memorable Moments Joe: "I might be multiple personas, but you never say there's a person, they're that persona. That's just wrong — morally, much less business-wise." Joe: "Dave has yet to find a situation in which talking about situations does not work." Dave's bathroom study: weather changed bathroom usage at French gas stations. It did not move the needle at Chicago train stations. Different situational markets. Aransas on the Paris Marathon: one toilet, a hundred urinals, 20,000 runners — half of whom needed to sit. A persona designed for one imagined customer, and the actual situation ignored. Joe on the American Girl Place men's bathroom stocking products that men do not use — because the company actually thought about who was walking in with their daughter. The Strategic Takeaway Companies need something they can count on. Personas have stopped being that thing. Aggregated situations — Friday night, business travel with kids, post-workout, end-of-quarter — are stable enough to plan against and dynamic enough to respect what the customer actually wants in the moment. AI no longer makes one-to-one a scary thing to attempt. The excuse is gone. The companies that move now will be the ones the customer feels actually understands them. Subscribe and Continue the Conversation Find the show on the Experience Strategist Substack, the podcast feed, and everywhere else. Article links in the show notes.

Today's clip is from episode 158 featuring Stefan Radev. In this conversation, Alex and Stefan explore a genuinely fascinating problem: how do you turn an expert's intuition into a mathematically valid prior distribution - and can AI help automate that process?Alex explains that prior elicitation is essentially a translation problem. Experts don't walk around thinking in probability distributions - their knowledge lives in intuitions, rules of thumb, and rough ranges. The challenge is converting that into something a Bayesian model can actually use.The traditional approach? Ask an expert for quantiles or a mean, then parameterize your prior with hyperparameters and simulate until the model-implied quantities match what the expert described. If your pipeline is differentiable end-to-end, you use gradient descent. If not, you fall back to something like Bayesian optimization. Either way, you're iterating toward a prior that genuinely reflects expert knowledge - not just a convenient assumption.But the really exciting part is what came next. In a follow-up paper, they pushed this further: instead of optimizing within a fixed parametric family (say, a Gaussian), they replaced the prior entirely with a normalizing flow - a flexible generative network - and ran the same procedure. No assumed distribution family. Just let the data and the expert's knowledge shape the prior from scratch.The catch? More flexibility means more non-identifiability and stability headaches. But the direction is clear: a fully automated, end-to-end pipeline for building priors from non-probabilistic expert knowledge. And in 2026, that pipeline could theoretically be driven by an agent.Get the full discussion hereSupport & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work

Send us Fan Mail*How do you forecast an event that has never happened before?*How do you forecast an event that has never happened before?The recent closure and reopening of the Strait of Hormuz are unique events. For events like these, traditional risk models lose their statistical basis: repetition. Alexander Denev returns to the podcast to show how causal models (Bayesian networks) let us reason about rare events despite this limitation.In this episode, we cover:- Why value-at-risk and other correlation-based models break exactly when you need them most- How a causal structure can "hold in time"- Building scenarios with LLMs - benefits, drawbacks, and lessons learned- Historical analogy as a modeling tool: Bosphorus, Hormuz, and more- A three-way robustness test for any Bayesian network- How the model's call held up: a ceasefire, a still-closed strait, and lasting infrastructure damage keeping oil elevated"History doesn't repeat itself, but it rhymes."------------------------------------------------------------------------------------------------------Video version available on the Youtube: https://youtu.be/FzKy2ws-7qsRecorded on May 29, 2026 in London, UK.------------------------------------------------------------------------------------------------------*About The Guest*Alexander Denev works at the intersection of quantitative finance, causality, and AI. He's the CEO of Turnleaf Analytics and the author of two books on applying Bayesian networks and probabilistic graphical models to finance and scenario analysis.Connect with Alexander:- Alexander on LinkedIn: https://www.linkedin.com/in/alexander-denev-66a25824/- Alexander's web page: https://turnleafanalytics.com/*About The Host*Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality (https://amzn.to/3QhsRz4 ).Connect with Alex:- Alex on the Internet: https://bit.ly/aleksander-molak*Links*Web- Alexander's LinkedIn post, Bayesian-network scenario for the Strait of Hormuz / Israel-Iran-US conflict: https://www.linkedin.com/posts/alexander-denev-66a25824_when-modelling-the-impact-of-events-that-share-7442892381668048896-JDs5/- Risk.net article, "Iran confusion makes the case for causal modelling": https://www.risk.net/our-take/7963361/iran-confusion-makes-the-case-for-causal-modellingBooks- Rebonato, R. & Denev, A. - Portfolio Management under Stress: A Bayesian-Net Approach to Coherent Asset Allocation (https://amzn.to/3vE6Jc1)- López de Prado, M. - Advances in Financial Machine Learning (https://amzn.to/3PXD8kH)- Molak, A. - Causal Inference and Discovery in Python (https://amzn.to/3VVK4m3)- Denev, A. - Probabilistic Graphical Models: A New Way of Thinking in Financial Modelling (https://amzn.to/3VQeLJm)- Pearl, J. & Mackenzie, D. - The Book of Why (recommended entry point) (https://amzn.to/4e0ATrZ)- Pearl, J. - Causality: Models, Reasoning and Inference (for advanced readers) (https://amzn.to/49zBKf5)- Rebonato, R. - Coherent Stress Testing: A Bayesian Approach to the Analysis of Financial Stress (https://amzn.to/3RC411e)*Perks & resources*

Yes, I mean whale privilege, aka fat women who demand attention. Lots of topics today, deadlifts, bayesian curls, wild meals and questions, solid rants. SUMMER SWOLE SPECIALS: https://summerswole.com

We sit down with Joshua Oommen to get nerdy about clinical reasoning, FDA standards, and why “good evidence” is harder to define than most of us admit. We challenge the reflex to trust p-values and meta-analyses, then test our instincts against real OBGYN examples where the literature has whiplashed practice. • why the podcast is called Thinking About OBGYN and how clinical reasoning shapes our work • the NEJM proposal to make one pivotal trial the FDA default and what “confirmatory evidence” might mean • medical reversal, surrogate endpoints, and how trust erodes when practice changes late • why Bayesian thinking fits how clinicians interpret tests, trials, and prior beliefs • how meta-analyses fail through small study effects, publication bias, p-hacking, and heterogeneity • the amnioinfusion comeback as a case study in applicability and overconfident conclusions Be sure to check out thinking about obgyn.com for more information and be sure to follow us on Instagram. 0:00 Welcome And Today's Big Question3:48 Why “Thinking About OBGYN” Exists11:54 The NEJM Push For One Trial16:38 Medical Reversal And Trust Problems24:43 AI Proteins And CRISPR Pressure Tests32:33 Bayes Thinking Beyond P Values36:43 Why Meta-Analyses Often Mislead41:08 Bias And Heterogeneity Red Flags46:24 Amnioinfusion And A Meta-Analysis Comeback1:02:29 Final Warnings And How To LearnFollow us on Instagram @thinkingaboutobgyn.

Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome workTakeaways:Q: Why are prior predictive checks so underused in practice, and how do simulations help?A: They're underused because researchers don't always think to run them before seeing data -- but also because doing them rigorously (in the style Michael Betancourt advocates, with prior push-forward checks on interpretable summaries) takes effort. Simulations make it cheap to generate thousands of “what-if world” datasets from your model and check whether they look plausible, catching bad priors before you ever touch real data.Q: How can generative AI help with prior elicitation?A: Rather than forcing a domain expert to choose a distributional family and parameterize it, you can use a generative model to translate their qualitative knowledge directly into a prior. The expert describes what realistic data should look like; the generative model produces synthetic datasets matching that description; those datasets are used to fit a prior distribution. It removes the assumption that experts can think in terms of parameters and replaces it with the more natural question: does this look like your data?Q: What would a foundation model for Bayesian inference actually look like?A: Stefan's bet is that it won't be a fine-tuned general LLM. The right analogy is chess: you don't fine-tune GPT to play chess, you teach it when to call Stockfish. For Bayesian inference, you'd want a semantic layer – an LLM that understands the analysis goal – calling specialized numerical engines (MCMC samplers, amortized inference networks) that do the actual computation. Agent skills are already a step in this direction; the longer-term vision is engines that have been trained from scratch to generalize across large families of models and priors.Full takeaways here.Chapters:00:00 How does amortized inference fit into modern Bayesian workflows?06:01 What role do simulations play across the full Bayesian workflow?12:12 How do you elicit priors from a domain expert who doesn't think in distributions?19:01 What would a foundation model for Bayesian inference actually look like?35:32 What is self-consistency in amortized inference and why does it matter?39:22 How does semi-supervised learning improve simulation-based inference?43:16 Why is sensitivity analysis so important yet so underused in Bayesian practice?47:40 What is multiverse analysis and how does it change how we report Bayesian results?51:32 How does amortized inference make sensitivity and multiverse analysis affordable?01:02:47 How do amortized inference and classical MCMC complement each other?01:10:08 What are the next major directions for BayesFlow and amortized inference research?Thank you to my Patrons for making this episode possible!Links from the show here.

Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters.SPONSOR:---Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open.Apply now: https://cyber.fund---Jordan trained as a statistician and cognitive scientist, and his career has been spent building machine learning systems that work in the real world: supply chains, commerce, healthcare, and large economic systems. When the field rebranded itself as AI and then AGI, he did not follow. Instead he argues that the framing is wrong. AI is better understood as a collective economic system than as a race to build a disembodied superintelligence.We talk about why AGI is mostly a PR term, what machine learning achieved before the LLM hype cycle, and why the assistant-on-your-shoulder vision may be less compelling than it sounds. Jordan explains why explanations need to be actionable, not merely mechanistic; why AlphaFold's missing error bars matter; how prediction-powered inference changes the picture; and why drug discovery is an incentive-design problem rather than a pure pattern-matching problem.ERRATA: Science magazine ranked him the most influential computer scientist, not Nature---TIMESTAMPS:00:00:00 Cold open: A demoralizing message to young builders00:02:04 CyberFund sponsor read00:02:50 From symbolic AI to machine learning systems00:05:42 Why AGI is mostly a PR term00:08:48 A collectivist, economic perspective on AI00:11:33 Why LLMs need system design, not hype00:14:50 Predictability beats faux understanding00:17:55 AlphaFold, bias, and prediction-powered inference00:21:48 Stop anthropomorphizing intelligence00:27:44 Drug discovery as an incentive problem00:32:29 The three-layer data market00:38:07 Social knowledge, markets, and culture00:45:39 Creator economics beyond Spotify00:48:30 How science-fiction AI narratives mislead young builders00:51:45 AI should improve humans, not replace them00:56:42 Safety is a property of the whole system00:58:12 Silicon Valley gurus and the cream off the top01:00:47 Game theory, mechanism design, and contracts01:04:39 Conformal prediction, e-values, and anytime inference01:08:11 A new liberal arts triangle for the AI era01:11:30 The Bayesian duck and markets as uncertainty reductionReScript (transcript, PDF, refs etc) - https://app.rescript.info/public/share/fb68f94af29d3745c6cf6125e01328b5---REFERENCES:person:[00:02:50] Michael I. Jordan (homepage)https://people.eecs.berkeley.edu/~jordan/paper:[00:06:01] A Collectivist, Economic Perspective on AIhttps://arxiv.org/abs/2507.06268[00:18:09] AlphaFoldhttps://www.nature.com/articles/s41586-021-03819-2[00:20:36] Prediction-Powered Inferencehttps://arxiv.org/abs/2301.09633[00:33:47] On Three-Layer Data Marketshttps://arxiv.org/abs/2402.09697[01:04:39] Conformal Prediction with Conditional Guaranteeshttps://arxiv.org/abs/2107.07511[01:04:51] A Tutorial on Conformal Predictionhttps://www.jmlr.org/papers/v9/shafer08a.html[01:06:00] E-Values Expand the Scope of Conformal Predictionhttps://arxiv.org/abs/2503.13050[01:08:23] Computational Thinkinghttps://www.cs.cmu.edu/~CompThink/papers/Wing06.pdfother:[00:28:20] How Should the FDA Test?https://rdi.berkeley.edu/events/sbc-assets/pdfs/Summit%20session%20speaker%20slides%20submission%20form-s1-5%20%28File%20responses%29/Slides%20in%20PDF%20%28Please%20name%20the%20submitted%20file%20as%20_firstname_-_lastname_-slides.pdf%29.%20%28File%20responses%29/27-Michael%20Jordan-Session%20V.pdf#page=15[00:28:40] Michael I. Jordan Session V Slides

In this Circles Off Q&A, Rob Pizzola is joined by Plus EV Analytics, Matt Buchalter, for a deep dive into sports betting modeling — how it actually works in practice, what separates good models from bad ones, and how sharp bettors think about building and evaluating their edge. This episode is built around 10 of the toughest modeling questions pulled directly from Circles Off content and community discussion. The conversation covers how to start a model from scratch, when a model is strong enough to bet real money, and how to deal with early season uncertainty like small samples, roster turnover, and regression questions. Rob and Matt also explore what matters more between getting the mean right or the distribution right, how to think about closing line value thresholds, and how to separate variance from a broken edge when results turn against you. They also get into Bayesian vs frequentist thinking, how professional bettors evaluate their models over time, and which inputs are often overrated or underrated when building a betting model. For anyone serious about sports betting models, market pricing, or long-term edge creation, this is a practical, sharp breakdown from two experienced voices in the space. Subscribe to Circles Off for more sharp betting conversations, modeling breakdowns, and market analysis.

For Episode 100 of the MOE Podcast, we're talking about Maine deer survival, winter severity and the state's apparent move toward a newer model for estimating winter impact and deer survival.The big question is simple: how is Maine measuring the real impact of winter on the deer herd, and how clearly is that information being explained to the public?This episode is not a personal attack on anyone at Maine IFW or within state government. It is constructive criticism. When people are in paid public positions and making decisions that affect wildlife management, hunting opportunity, and Maine's outdoor traditions, the public deserves clear explanations, timely communication, and transparency about the data and models being used.From the outside looking in, the state often seems slow or scant in providing information on these issues. If new statistical models, including Bayesian-style approaches, are being used to estimate deer survival or winter impact, then hunters, landowners, and the public should be able to understand the basic assumptions, uncertainty, and management implications.We also use this conversation to connect the topic to broader lessons from 100 episodes: learning from incomplete information, staying humble, updating what we believe, and asking better questions about the Maine outdoors.Here's to #100!#MaineOutdoorEnthusiast #MaineOutdoors #MaineDeer #DeerHunting #WhitetailDeer #MaineHunting #WildlifeManagement #DeerWinteringAreas #BayesianStatistics #WinterSeverity #MaineIFW #outdoorpodcast Check us out on the web at:https://www.maineoutdoorenthusiast.comContact:maineoutdoorenthusiast@gmail.com

If you enjoy this episode, we're sure you will enjoy more content like this on The Occult Rejects.  In fact, we have curated playlists on occult topics like grimoires, esoteric concepts and phenomena, occult history, analyzing true crime and cults with an occult lens, Para politics, and occultism in music. Whether you enjoy consuming your content visually or via audio, we've got you covered - and it will always be provided free of charge.  So, if you enjoy what we do and want to support our work of providing accessible, free content on various platforms, please consider making a donation to the links provided below.  Thank you and enjoy the episode!Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Cash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejectsFull show-notes bibliographyCore EEG and oscillationsAbubaker, M., & Dankaerts, W. (2021). Working memory and cross-frequency coupling of neuronal oscillations. *Frontiers in Psychology, 12*, 742860.Axmacher, N., Henseler, M. M., Jensen, O., Weinreich, I., Elger, C. E., & Fell, J. (2010). Cross-frequency coupling supports multi-item working memory in the human hippocampus. *Proceedings of the National Academy of Sciences, 107*(7), 3228–3233.Jensen, O., & Mazaheri, A. (2010). Shaping functional architecture by oscillatory alpha activity: Gating by inhibition. *Frontiers in Human Neuroscience, 4*, 186.Rayi, A., et al. (2022). Electroencephalogram. *StatPearls*. StatPearls Publishing.StatPearls / NCBI Bookshelf. (2024). Introduction to electroencephalography (EEG). *NCBI Bookshelf*.Theta, alpha, beta, gamma, and controlCavanagh, J. F., & Shackman, A. J. (2015). Frontal midline theta reflects anxiety and cognitive control: Meta-analytic evidence. *Journal of Physiology-Paris, 109*(1–3), 3–15.Eisma, J., et al. (2021). Frontal midline theta differentiates separate cognitive control strategies while still generalizing the need for cognitive control. *Scientific Reports, 11*, 14641.Jensen, O., Bonnefond, M., & VanRullen, R. (2012). An oscillatory mechanism for prioritizing salient unattended stimuli. *Trends in Cognitive Sciences, 16*(4), 200–206.Lundqvist, M., Herman, P., & Miller, E. K. (2018). Working memory: Delay activity, yes! Persistent activity? Maybe not. *Journal of Neuroscience, 38*(32), 7013–7019.Sleep architecture, spindles, and memoryCaporro, M., Haneef, Z., Yeh, H.-J., Mohamed, F. B., & Levin, H. S. (2012). Functional MRI of sleep spindles and K-complexes. *Clinical Neurophysiology, 123*(2), 303–309.Chen, P., Miao, X., Chen, J., et al. (2023). The devastating effects of sleep deprivation on memory: Lessons from rodent models, aging, and Alzheimer's disease. *Frontiers in Neuroscience, 17*, 1151639.Ng, T., et al. (2025). Bayesian meta-analysis reveals the mechanistic role of slow oscillation-spindle coupling in sleep-dependent memory consolidation. *eLife, 13*, RP101992.Patel, A. K., et al. (2024). Physiology, sleep stages. *StatPearls*. StatPearls Publishing.Páez, A., Gillman, S. O., Dogaheh, S. B., et al. (2025). Sleep spindles and slow oscillations predict cognition and biomarkers of neurodegeneration in mild to moderate Alzheimer's disease. *Alzheimer's & Dementia, 21*, e14424.Hypnagogia, N1, and dream incubationHorowitz, A. H., Esfahany, S., Boyle, M. R., et al. (2023). Targeted dream incubation at sleep onset increases post-sleep creative performance. *Scientific Reports, 13*, 5055.Lacaux, C., Andrillon, T., Bastoul, D., et al. (2021). Sleep onset is a creative sweet spot. *Science Advances, 7*(50), eabj5866.Meditation, prayer, chanting, and yoga nidraDatta, K., Mallick, H. N., Tripathi, M., Ahuja, G. K., & Deepak, K. K. (2022). Electrophysiological evidence of local sleep during yoga nidra practice in young male volunteers. *Frontiers in Neurology, 13*, 910794.Dobrakowski, P., Błaszkiewicz, M., & Skalski, S. (2020). Changes in the electrical activity of the brain in the alpha and theta bands during prayer and meditation. *International Journal of Environmental Research and Public Health, 17*(24), 9567.Gao, J., Leung, H. K., Wu, B. W. Y., Skouras, S., & Sik, H. H. (2019). The neurophysiological correlates of religious chanting. *Scientific Reports, 9*, 4262.Kaur, C., & Singh, P. (2015). EEG derived neuronal dynamics during meditation: Progress and challenges. *Advances in Preventive Medicine, 2015*, 614723.Lomas, T., Ivtzan, I., & Fu, C. H. Y. (2015). A systematic review of the neurophysiology of mindfulness on EEG oscillations. *Neuroscience & Biobehavioral Reviews, 57*, 401–410.Hypnosis and suggestionJensen, M. P., Adachi, T., & Hakimian, S. (2015). Brain oscillations, hypnosis, and hypnotizability. *American Journal of Clinical Hypnosis, 57*(3), 230–253.Kirenskaya, A. V., Novototsky-Vlasov, V. Y., Chistyakov, A. V., & Zvonikov, V. M. (2011). Waking EEG spectral power and coherence differences between highly hypnotizable and low hypnotizable subjects. *International Journal of Clinical and Experimental Hypnosis, 59*(2), 144–164.Mendoza, M. E., & Capafons, A. (2024). Neural correlates of hypnosis: A systematic narrative review. *Frontiers in Psychology, 15*, 1327738.Ritual rhythm, trance, and synchronyHuels, E. R., Kim, H. S., Lee, U., & Mollaahmetoglu, O. M. (2021). Neural correlates of the shamanic state of consciousness. *Frontiers in Human Neuroscience, 15*, 610466.Mogan, R., Fischer, R., & Bulbulia, J. A. (2017). To be in synchrony or not? A meta-analysis of synchrony's effects on behavior, perception, cognition and affect. *Journal of Experimental Social Psychology, 72*, 13–20.Tarr, B., Launay, J., & Dunbar, R. I. M. (2016). Silent disco: Dancing in synchrony leads to elevated pain thresholds and social closeness. *Evolution and Human Behavior, 37*(5), 343–349.Entrainment, binaural beats, fatigue, and overloadGoodman, S. P. J., et al. (2025). Approaches to inducing mental fatigue: A systematic review and meta-analysis of (neuro)physiologic indices. *Neuroscience & Biobehavioral Reviews, 170*, 105957.Ingendoh, R. M., Posny, E. S., & Heine, A. (2023). Binaural beats to entrain the brain? A systematic review of the effects of binaural beat stimulation on brain oscillatory activity, and the implications for psychological research and intervention. *PLOS ONE, 18*(5), e0286023.Snipes, S., et al. (2024). Extended wakefulness alters the relationship between EEG theta and alpha bursts and behavioural outcome. *European Journal of Neuroscience, 60*(8), 6268–6284.Xiang, C., et al. (2024). A resting-state EEG dataset for sleep deprivation. *Scientific Data, 11*, 406.Parkinson's disease and pathological betaAsadi, A., et al. (2022). The origin of abnormal beta oscillations in the parkinsonian corticobasal ganglia circuit. *Frontiers in Neuroscience, 16*, 823719.Paulo, D. L., et al. (2023). Corticostriatal beta oscillation changes associated with cognitive function in Parkinson's disease. *NPJ Parkinson's Disease, 9*, 202.Ancient sleep, dreams, and Asclepian healingAskitopoulou, H. (2015). Sleep and dreams: From myth to medicine in ancient Greece. *Journal of Anesthesia History, 1*(3), 70–75.Kapotsis, G., & Steiropoulos, P. (2025). Sleep incubation [enkoimesis] in medical practice at Asclepieia of Ancient Greece — the Ancient Greek sleep medicine. *Sleep Medicine, 130*, 85–89.Pavli, A. (2024). Asclepieia in ancient Greece: pilgrimage and healing. *Journal of Integrative Medicine and Research, 3*(2), 100119.Also want to remind people about the website, if you're into reading we have tons of information by multiple contributors, and we got t-shirts up on the site if you're interested. Fun fact, the art is all based on the eyeball. A

Today's clip is from Episode 157 featuring Stefan Radev. In this conversation, Alex and Stefan dig into one of the hardest open problems in simulation-based inference — hierarchical models.The core idea: when you move from flat to hierarchical models, you're no longer estimating one set of parameters. You have local parameters that vary by location (or subject, or city) and global parameters that capture what's shared across all of them. And you don't just want each separately — you want the full joint posterior, because that's where the Bayesian magic of shrinkage actually lives.Stefan builds the problem from the ground up. Start with the simplest hierarchical case: a two-level model. He uses electoral forecasting in France as the example — cities nested inside departments nested inside the whole country.Now your simulator has to cover all three levels. If that simulator is slow (think: brain emulators, minutes per sample), scaling to hundreds of groups becomes completely intractable. Memory issues, specialized network requirements, the works.The key insight: this problem has structure you can exploit. The joint posterior factorizes in a particularly nice way — each local parameter depends on its own local data and on the global parameters. That means instead of cramming everything into one giant high-dimensional vector and hoping a neural network figures it out, you can decompose the problem. Estimate local parameters conditioned on local data and the globals. Use composition.The takeaway: hierarchical models aren't just "harder flat models" - they have a geometry that demands a different architecture. Respecting that structure is what makes amortized inference scale.Get the full discussion hereSupport & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work

Cortisol after cancer is the conversation nobody on my care team had with me. I was diagnosed with breast cancer in 2021 — invasive ductal carcinoma, stage one, grade two. I went through lumpectomy, radiation, ovarian suppression, and two years on an aromatase inhibitor before I had to come off because my bones were already in osteoporosis. Throughout all of it, my nervous system was screaming. My cortisol was running hot all day long, confirmed by a Dutch test. And not one doctor told me what stress was doing to my body or how to mitigate it. In this solo episode of Not Today Cancer, I'm walking you through the seven activities that lowered my cortisol...broken into the things that don't cost a dime (meditation, breathwork, walking outside, unplugging) and the things that do (acupuncture, energy healing, therapy). I'm also sharing the actual research behind each one, so you know this isn't woo...it's documented science.  What you'll learn: • Why cortisol is wrecked after a cancer diagnosis (and why mine was high long before) • The symptoms of high cortisol most breast cancer survivors miss • How mindfulness meditation protected the cortisol rhythm of breast cancer survivors in a randomized controlled trial • Why a single session of slow breathing drops cortisol immediately • The "nature pill" research showing 20–30 minutes outside lowers cortisol 21% per hour • Why the NCCN officially recommends acupuncture for cancer survivors If you're a breast cancer survivor, caregiver, or anyone whose body has been running on fumes...this episode is for you. We don't get the option of not mitigating stress. Pick one thing on this list and start tomorrow. Disclaimer: This episode reflects my personal experience and a summary of public research. It is not medical advice. Always consult your care team.

Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome workTakeaways:Q: What is simulation-based inference and what does "sim-to-real" mean?A: Simulation-based inference (SBI) uses a mechanistic simulator as an epistemic tool: you train a neural network on a large number of labeled simulations and then deploy it on real, unlabeled data. The "sim-to-real" framing captures the key asymmetry -- your network never sees real data during training, only simulations, but it generalizes to real observations at inference time. This is the opposite of the more common "synthetic-for-ML" approach, where fake data is used purely to augment real training data.Q: What is the amortized inference agent skill and what does it do?A: It's an open-source AI agent skill, co-developed by Stefan and Alexandre, that teaches an AI coding agent to run a complete, state-of-the-art amortized inference workflow. Because amortized inference is recent enough that it's underrepresented in LLM training data, vanilla agents tend to get it wrong. The skill injects the right methodology: it guides the agent to set up the simulator, choose the right network architecture, run a pilot, train with appropriate diagnostics, and produce an actionable report -- without the user needing to know the details.Q: What is calibration coverage and why should you never skip it?A: Calibration coverage tells you whether your posterior uncertainty is honest -- whether your credible intervals actually contain the true parameter at the right frequency. A model can show poor parameter recovery yet still be well-calibrated (because it's falling back on the prior), or it can appear to recover parameters while being poorly calibrated. Running calibration diagnostics both in-sample and out-of-sample is especially revealing for hierarchical models, which often appear to underfit in-sample but generalize much better out-of-sample thanks to shrinkage.Full takeaways hereChapters:00:00:00 How does amortized inference fit into the Bayesian workflow?00:12:03 What does "sim-to-real" mean in simulation-based inference?00:15:57 Why is amortized inference particularly suited to psychology and neuroscience?00:21:51 What is the amortized inference agent skill?00:39:00 What is calibration coverage and how do you interpret it?00:41:50 How do you decide what to do next after your first training run?00:44:53 How do actionable insights make Bayesian workflows more usable?00:49:08 What are the unique challenges of hierarchical models in amortized inference?01:00:51 What is the current state of BayesFlow's support for hierarchical models?01:05:00 What are the main failure modes of amortized inference and how do you handle model misspecification?Thank you to my Patrons for making this episode possible!Links from the show