Podcasts about markov chains

In this episode, Kendall and rachel talk about: * rachel's need to finish things and coping mechanisms around that * The recent spate of cold weather/storms and a tale of Kendall's friend and the whisky night * (This podcast may be sponsored by Friday Deployment Spirits, i guess) * An unsurprising objection regarding terminology * How Kendall is obviously a Markov Chain bot, and how have i never noticed this before * The fact that AI-directed research is nothing new * Public access to AI-type tools and how it has expanded the hype cycle * Environmental concerns around resources required * Robots and why they should just exist to do stuff humans don't want to do * Implications of the mismatch in public expectations vs the reality of what AI is actually capable of * Losing the ability of finding the needles of useful information in the haystack of real and generated content * How the hype is definitely hype but it should still be in your pitch deck * The word rachel was looking for: Captcha * Usefulness as a writing tool, but not as an author * Kendall's tantalizing taste of socialized medicine * rachel recommends: Shelter Point distillery's Smoke Point whiskey (Vancouver Island, BC) * Kendall recommends: barrel-finished/aged gins * rachel also recommends: The Future by Naomi Alderman * Kendall also recommends: Brandon Sanderson's The Way of Kings, Margaret Atwood's The Handmaid's Tale. Special thanks to Mel Stanley for our theme music

In this episode, the boys talk about a mathematical hypothesis and its tentacles that help describe probability with practical implications for drivers, gamblers, and anyone with money invested. _____________________________________________ Connect with the show: Reddit: https://www.reddit.com/r/gametheorypod/comments/w35b8d/welcome_to_rgametheorypod/ Website: https://www.gametheorypod.com Game Theory on Facebook: https://www.facebook.com/gametheorypod Game Theory on Twitter: @GameTheoryPod https://twitter.com/GameTheoryPod Nick on Twitter: @tribnic https://twitter.com/tribnic Chris on Twitter: @ChrisAndrews315 https://twitter.com/ChrisAndrews315 _____________________________________________ Googliography : Markov Chain:https://en.wikipedia.org/wiki/Markov_chain Random Walk: https://en.wikipedia.org/wiki/Random_walk Martingale Strategy: https://en.wikipedia.org/wiki/Martingale_(probability_theory) --- Send in a voice message: https://podcasters.spotify.com/pod/show/gametheory/message

In episode 37 of the quantum consciousness series, Justin Riddle takes a deep dive into Donald Hoffman's conscious agent model and relates it to the leading theories of quantum consciousness. The structure of this episode is an introduction to Hoffman's model of conscious agents, then an interview with Don Hoffman in November 2022, and finally some reflections on the implications of this model. Hoffman begins by describing the interface theory of perception: we have mistaken the external “physical” world to be fundamental reality. But this external world that we see around us is an evolved interface that was created through billions of years of evolution and cannot be trusted. The world you experience is like a video game – with icons, side quests, and abstract motivations to win victory points. The “real” world is not directly accessible to us through our perceptual systems and there is a great illusion at play. Hoffman then proposes his Conscious Agents theory, in which the universe is comprised of conscious beings interacting with each other. He describes these conscious agents as Markov Chains – probabilistic systems that move through a set of possible experience and action states while learning from their interactions with the world at large. Finally, he proposes that conscious agents are composed of conscious agents resulting in a fractal nested hierarchy of beings from the scale of the entire universe down to the Planck scale. This nested hierarchy is fundamental and now just needs to be mapped into modern particle physics in order to complete his theory of everything. Here, he introduces “decorated permutations” which are a way to map the Markov Models of his conscious agents into geometric structures. With this mapping, he claims to connect his agents to fundamental geometric forms at the core of reality, such as the amplituhedron, and then that amplituhedron can derive space-time, particle physics, and quantum mechanics. His theory is very Platonist in its essence and relies on a geometric depiction of reality. At the end of the episode, I praise the ability of Hoffman's theory to connect the nested hierarchies of beings into a substrate for mathematical forms to arise, but also caution that his model throws away the physical world and mental world to some degree to focus exclusively on the Platonic world of forms. Living within a hyperdimensional geometric form may result in the same nihilistic conclusions that our lives are just unfolding as sub-projections of this universal form. Can we salvage the human spirit from unmoving crystalline geometry? I hope you enjoy!

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In this episode, we talk about optics and how Zeiss uses Markov Chains in manufacturing. We also talk about an AI engineering platform and some rumors in the chip industry.

In this episode, our guest is Sean Meyn, Professor and Robert C. Pittman Eminent Scholar Chair in the Department of Electrical and Computer Engineering at the University of Florida. The episode features Sean's adventures in the areas of Markov chains, networks and Reinforcement Learning (RL) as well as anecdotes and trivia about beekeeping and jazz.Outline00:00 - Intro00:22 - Sean's early steps03:53 - Markov chains08:45 - Networks18:26 - Stochastic approximation25:00 - Reinforcement Learning38:57 - The intersection of Reinforcement Learning and  Control42:37 - Favourite theorem44:05 - Beekeeping and jazz48:47 - OutroEpisode linksSean's website: https://meyn.ece.ufl.edu/Sean's books: shorturl.at/CFGRY (and T. Sargent's review: shorturl.at/hlGNR)G. Zames: shorturl.at/JPRWX (see also: shorturl.at/chiw5)State space model: shorturl.at/hST07 The life and work of A.A. Markov: shorturl.at/qsv35Fluid model: shorturl.at/HKN56M/M/1 queue: shorturl.at/dQW36Borkar-Meyn theorem: shorturl.at/eSTV4NCCR Automation Symposia: shorturl.at/csv03 (see also shorturl.at/ekpZ3)V. Konda's PhD Thesis: shorturl.at/bdrv7Podcast infoPodcast website: https://www.incontrolpodcast.com/ Apple Podcasts: https://podcasts.apple.com/us/podcast/incontrol/id1624068002 Spotify: https://open.spotify.com/show/7dZvt77XNtHxyrFqM8YTwf RSS: https://feeds.buzzsprout.com/1632769.rss Youtube: https://www.youtube.com/channel/UCl83hwBSVRLYj2NWS08P9bg/featured Facebook: https://www.facebook.com/InControl-podcast-114303337936834 Twitter: https://twitter.com/IncontrolP Instagram: https://www.instagram.com/incontrol_podcast/ Patreon: https://www.patreon.com/incontrolpodcast/Acknowledgments and sponsorsThis episode was supported by the National Centre of Competence in Research on «Dependable, ubiquitous automation» and the IFAC Activity fund.The podcast benefits from the help of an incredibly talented and passionate team. Special thanks to A. Bastani, B. Sawicki, E. Cahard, F. Banis, F. Dörfler, J. Lygeros, as well as the ETH  and mirrorlake studios. Music was composed by A New Element.Support the show

We talk a lot about generative modeling on this podcast — at least since episode 6, with Michael Betancourt! And an area where this way of modeling is particularly useful is healthcare, as Maria Skoularidou will tell us in this episode. Maria is a final year PhD student at the University of Cambridge. Her thesis is focused on probabilistic machine learning and, more precisely, towards using generative modeling in… you guessed it: healthcare! But her fields of interest are diverse: from theory and methodology of machine intelligence to Bayesian inference; from theoretical computer science to information theory — Maria is knowledgeable in a lot of topics! That's why I also had to ask her about mixture models, a category of models that she uses frequently. Prior to her PhD, Maria studied Computer Science and Statistical Science at Athens University of Economics and Business. She's also invested in several efforts to bring more diversity and accessibility in the data science world. When she's not working on all this, you'll find her playing the ney, trekking or rawing. Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) ! Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton and Jeannine Sue. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Maria on Twitter: https://twitter.com/skoularidou (https://twitter.com/skoularidou) Maria on LinkedIn: https://www.linkedin.com/in/maria-skoularidou-1289b62a/ (https://www.linkedin.com/in/maria-skoularidou-1289b62a/) Maria's webpage: https://www.mrc-bsu.cam.ac.uk/people/in-alphabetical-order/n-to-s/maria-skoularidou/ (https://www.mrc-bsu.cam.ac.uk/people/in-alphabetical-order/n-to-s/maria-skoularidou/) Mixture models in PyMC: https://www.pymc.io/projects/examples/en/latest/gallery.html#mixture-models (https://www.pymc.io/projects/examples/en/latest/gallery.html#mixture-models) LBS #4 Dirichlet Processes and Neurodegenerative Diseases, with Karin Knudson: https://learnbayesstats.com/episode/4-dirichlet-processes-and-neurodegenerative-diseases-with-karin-knudson/ (https://learnbayesstats.com/episode/4-dirichlet-processes-and-neurodegenerative-diseases-with-karin-knudson/) Bayesian mixtures with an unknown number of components: https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00095 (https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00095) Markov Chain sampling methods for Dirichlet Processes: https://www.tandfonline.com/doi/abs/10.1080/10618600.2000.10474879 (https://www.tandfonline.com/doi/abs/10.1080/10618600.2000.10474879) Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models: https://academic.oup.com/biomet/article-abstract/95/1/169/219181...

As tastytraders constantly searching for advantages in the market, it is clear that some markets present us with more opportunities, and some markets present us with fewer opportunities. And in the world of high-level math and statistics, Markov Chains,  Hidden Markov Models, and now Markov Switching Models illustrate how the randomness and unpredictability of the market can be categorized into different regimes. Specifically, if we just focus on selling premium during down days, as our research has studied, this type of market regime yields far more opportunity than a non-down day.

As tastytraders constantly searching for advantages in the market, it is clear that some markets present us with more opportunities, and some markets present us with fewer opportunities. And in the world of high-level math and statistics, Markov Chains,  Hidden Markov Models, and now Markov Switching Models illustrate how the randomness and unpredictability of the market can be categorized into different regimes. Specifically, if we just focus on selling premium during down days, as our research has studied, this type of market regime yields far more opportunity than a non-down day.

What do some of the famous arguments for God's existence have in common? They're all deductive arguments. They are air-tight in their formulation. The first step to truly understanding these arguments is understanding the nature of deductive arguments. What does it take to disprove them? What makes them air-tight? Join us to find out!Chapter markers: 00:00 Intro01:14 What are Deductive Arguments?02:45 Will Caesar die from cyanide?03:40 If Premise 1 is true will Premise 2 also be true?06:48 What makes a Deductive Argument valid?08:40 Greek= language/ethnicity/cuisine??11:45 What is Affirming the Consequent fallacy?13:26 How to disprove the Fine Tuning Argument?16:19 Is the Markov Chain rule connected to Deductive Arguments?18:24 The burden of formulating Deductive Arguments!20:50 What is the resource for the Natural Theology series?23:15 What's up next?23:44 OutroLinks and citation:Record a question and stand a chance to be featured on SAFT Podcast (https://www.speakpipe.com/saftpodcast)  (Podcast) Check out ‘Ep #59- Why & How Should I Show Christianity Is True?' (https://saftpodcast.buzzsprout.com/1034671/9268542-ep-59-why-how-should-i-show-christianity-is-true)(Book) Get hold of 'Reasonable Faith' (https://www.amazon.com/Reasonable-Faith-3rd-Christian-Apologetics-ebook/dp/B00G5M1BFK)Interested to join our voluntary team as Graphic Designer? Reach out to us at ankit@saftapologetics.comWatch the video podcast here (https://youtu.be/uDVtxbTUdy8)Equipping the believer defend their faith anytime, anywhere. Our vision is to do so beyond all language barriers in India and beyond!SAFT Apologetics stands for Seeking Answers Finding Truth and was formed off inspiration from the late Nabeel Qureshi's autobiography that captured his life journey where he followed truth where it led him. We too aim to be a beacon emulating his life's commitment towards following truth wherever it leads us.Website: https://www.saftapologetics.com/Instagram: https://www.instagram.com/saftapologetics/Facebook: https://www.facebook.com/saftapologetics/Newsletter: http://www.sendfox.com/saftapologeticsPatreon: https://www.patreon.com/saftapologetics/Is there a question that you would like to share with us?Send us your questions, suggestions and queries at: info@saftapologetics.com

Colin Davy, data scientist at Facebook and two time winner of the Sloan Sports Analytics Conference Hackathon, joins me to talk about his custom golf model for the Masters. He describes how he uses Markov Chains to predict the outcome of golf and how this differs from the Strokes Gained approach. He predicts which golfers have the highest probability to win the 2021 Masters. Finally, Colin talks about how he used data to become a Jeopardy champion.

Okay enough messing around, this week we get into the Matrix. Okay not that matrix. The mathematical matrix. But this one is way more powerful than a dystopian future in which humanity is unknowingly trapped inside a simulated reality. That's piddly. Mathematical matrices are used in everywhere, from making computer games to quantum physics.That's Jane Breen ,Assistant Professor in Applied Maths in Ontario University in Canada. She loves modelling the complexity of networks in the real world with some very powerful and sometimes simple tools. Speaking of simple tools, before long, I start throw around lingo like Eigenvalues and Markov Chains like I know what I'm talking about. We find out how Google got so successful, a brief digression into how drugmakers know their drugs will work and before finishing off on how to control the spread of disease. And Ruby and Lily find themselves playing with a real-life application of a Markov Chain, a Game of Snakes and Ladders. Jane Breen https://sites.google.com/view/breenjA really good youtube channel for visualising what's going on in Matrices and All Of That. https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

A wall arch of a pioneer in the development of a pioneer in the development of sound film recording used for motion pictures. https://slartyblog.wordpress.com/ --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/tadpole-slamp/support

In this video, I talk to my good friend and former colleague of mine Gianfranco Bino who was in my classes when I was at York University studying astrophysics. Franco and I worked on the same project together, in different wings of the research, and he has gone on to get his master's in Applied Mathematics at Western University. Franco is now currently finishing his Ph.D. in applied mathematics and has landed his dream job as a quantitative analyst at a bank in Toronto. In this podcast, I ask him about his work, his past, and how he got to where he is today. I try to keep the content as easy to consume as possible, so I'm hoping you all enjoy the video, and leave any questions for myself, or for Franco, in the comments below. @ 0:00 Introduction: Gianfranco Bino Ph.D. Applied Mathematics & Quantitative Analyst @ 1:05 Quantitative Analytics: Mathematical & Algorithmic Development for Financial Products @ 3:12 Fixed Income, High Yield High-Risk Bonds and Predictive Modelling @ 5:45 Risk Assessment: Parameters of The Model and How to Analyze Assessment @ 6:55 Markov Mixture Models: Credit Migration Using Markovian Statistics @ 8:53 Markov Chains & Brownian Motion: Stochastic Processes and Randomness @ 11:32 The Randomness of Financial Modelling: The Chaos in Economics and Financial Data @ 14:07 Predictive Error Mitigation: Reducing Risk & Credit Rating Formulation @ 18:16 Algorithmic Trading: Growth in The Sector Using New Techniques @ 21:08 History and Background: Motivation, Limiting Factors & Drive @ 22:46 The Schematics & Organization: Creating a Working Entity By Design @ 24:44 Astrophysics & Mathematics: How Studying The Universe Ties In @ 28:26 Prestellar & Protostellar Cores: A Research on Magnetization States @ 31:34 An Astrophysicists Interpretation: Alien Life & Pondering of The Universe @ 35:35 Predicting Volatility: Market Dynamics & The Computer Models @ 38:06 Human Predictive Intelligence & The Effects of Artificial Intelligence ( AI ) & Machine Learning @ 40:47 The Final Model of Predictive Analytics: Prospects of Developing An All in One Solution @ 46:45 Outro: Final Conclusions

Suzanne joins us as we review Ohanami, Marvel United, Hierarchy, Micro City, Finished!, and Inhuman Conditions.  Geoff talks about Markov Chains, we talk about dessert and games, skirmish games, and other questions.  And finally, we end the show talking about games that defy "Vasel's Law" which states 'Every great game will eventually be reprinted'.

In this Marketing Over Coffee: Learn about Attribution, Pumpkin Spice, Flying Planes, and more! Direct Link to File Brought to you by our sponsors: Otis and LinkedIn Attribution and the Digital Customer Journey! Shapley Game Theory vs. Markov Chains vs. Propensity score matching 7:05 – 8:57 Easily create ads for Facebook, Instagram, and Google right […] The post Pumpkin Spice Pilots, and Marketing AI Tactics Part 4 of 5 appeared first on Marketing Over Coffee Marketing Podcast.

Markov Chains were once very useful - particularly before we had the computing power we have today. Basically, Markov Chains allow you to create a number of states (fully functional, degraded, REALLY degraded, failed, and so on ...) and then model constant transition rates between each. Which nominally allows you to model failure AND repair - but in practice this could be a little too simple to help your design decisions today. Want to learn more? Listen here! The post SOR 495 Markov Chain Modeling – Just the Basics appeared first on Accendo Reliability.

Show Notes:(2:08) Mael recalled his experience getting a Bachelor of Science Degree in Economics from HEC Lausanne in Switzerland.(4:47) Mael discussed his experience co-founding Wanago, which is the world’s first van acquisition and conversion crowdfunding platform.(9:48) Mael talked about his decision to pursue a Master’s degree in Actuarial Science, also at HEC Lausanne.(11:51) Mael talked about his teaching assistantships experience for courses in Corporate and Public Finance.(13:30) Mael talked about his 6-month internship at Vaudoise Assurances, in which he focused on an individual non-life product pricing.(16:26) Mael gave his insights on the state of adopting new tools in the actuarial science space.(18:12) Mael briefly went over his decision to do a Post Master’s program in Big Data at Telecom Paris, which focuses on statistics, machine learning, deep learning, reinforcement learning, and programming.(20:51) Mael explained the end-to-end process of a deep learning research project for the French employment center on multi-modal emotion recognition, where his team delivered state-of-the-art models in text, sound, and video processing for sentiment analysis (check out the GitHub repo).(26:12) Mael talked about his 6-month part-time internship doing Natural Language Processing for Veamly, a productivity app for engineers.(28:58) Mael talked about his involvement with VIVADATA, a specialized AI programming school in Paris, as a machine learning instructor.(34:18) Mael discussed his current responsibilities at Anasen, a Paris-based startup backed by Y Combinator back in 2017.(38:12) Mael talked about his interest in machine learning for healthcare, and his goal to pursue a Ph.D. degree.(40:00) Mael provided a neat summary on current state of data engineering technologies, referring to his list of in-depth Data Engineering Articles.(42:36) Mael discussed his NoSQL Big Data Project, in which he built a Cassandra architecture for the GDELT database.(47:38) Mael talked about his generic process of writing technical content (check out his Machine Learning Tutorials GitHub Repo).(52:50) Mael discussed 2 machine learning projects that I personally found to be very interesting: (1) a Language Recognition App built using Markov Chains and likelihood decoding algorithms, and (2) the Data Visualization of French traffic accidents database built with D3, Python, Flask, and Altair.(56:13) Mael discussed his resources to learn deep learning (check out his Deep Learning articles on the theory of deep learning, different architectures of deep neural networks, and the applications in Natural Language Processing / Computer Vision).(57:33) Mael mentioned 2 impressive computer vision projects that he did: (1) a series of face classification algorithms using deep learning architectures, and (2) face detection algorithms using OpenCV.(59:47) Mael moved on to talk about his NLP project fsText, a few-shot learning text classification library on GitHub, using pre-trained embeddings and Siamese networks.(01:03:09) Mael went over applications of Reinforcement Learning that he is excited about (check out his recent Reinforcement Learning Articles).(01:05:14) Mael shared his advice for people who want to get into freelance technical writing.(01:06:47) Mael shared his thoughts on the tech and data community in Paris.(01:07:49) Closing segment.His Contact Info:TwitterWebsiteLinkedInGitHubMediumHis Recommended Resources:Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron CourvillePyImageSearch by Adrian RosebrockStation F Incubator in ParisBenevolentAIEconometrics Data Science: A Predictive Modeling Approach by Francis Diebold

Show Notes:(2:08) Mael recalled his experience getting a Bachelor of Science Degree in Economics from HEC Lausanne in Switzerland.(4:47) Mael discussed his experience co-founding Wanago, which is the world’s first van acquisition and conversion crowdfunding platform.(9:48) Mael talked about his decision to pursue a Master’s degree in Actuarial Science, also at HEC Lausanne.(11:51) Mael talked about his teaching assistantships experience for courses in Corporate and Public Finance.(13:30) Mael talked about his 6-month internship at Vaudoise Assurances, in which he focused on an individual non-life product pricing.(16:26) Mael gave his insights on the state of adopting new tools in the actuarial science space.(18:12) Mael briefly went over his decision to do a Post Master’s program in Big Data at Telecom Paris, which focuses on statistics, machine learning, deep learning, reinforcement learning, and programming.(20:51) Mael explained the end-to-end process of a deep learning research project for the French employment center on multi-modal emotion recognition, where his team delivered state-of-the-art models in text, sound, and video processing for sentiment analysis (check out the GitHub repo).(26:12) Mael talked about his 6-month part-time internship doing Natural Language Processing for Veamly, a productivity app for engineers.(28:58) Mael talked about his involvement with VIVADATA, a specialized AI programming school in Paris, as a machine learning instructor.(34:18) Mael discussed his current responsibilities at Anasen, a Paris-based startup backed by Y Combinator back in 2017.(38:12) Mael talked about his interest in machine learning for healthcare, and his goal to pursue a Ph.D. degree.(40:00) Mael provided a neat summary on current state of data engineering technologies, referring to his list of in-depth Data Engineering Articles.(42:36) Mael discussed his NoSQL Big Data Project, in which he built a Cassandra architecture for the GDELT database.(47:38) Mael talked about his generic process of writing technical content (check out his Machine Learning Tutorials GitHub Repo).(52:50) Mael discussed 2 machine learning projects that I personally found to be very interesting: (1) a Language Recognition App built using Markov Chains and likelihood decoding algorithms, and (2) the Data Visualization of French traffic accidents database built with D3, Python, Flask, and Altair.(56:13) Mael discussed his resources to learn deep learning (check out his Deep Learning articles on the theory of deep learning, different architectures of deep neural networks, and the applications in Natural Language Processing / Computer Vision).(57:33) Mael mentioned 2 impressive computer vision projects that he did: (1) a series of face classification algorithms using deep learning architectures, and (2) face detection algorithms using OpenCV.(59:47) Mael moved on to talk about his NLP project fsText, a few-shot learning text classification library on GitHub, using pre-trained embeddings and Siamese networks.(01:03:09) Mael went over applications of Reinforcement Learning that he is excited about (check out his recent Reinforcement Learning Articles).(01:05:14) Mael shared his advice for people who want to get into freelance technical writing.(01:06:47) Mael shared his thoughts on the tech and data community in Paris.(01:07:49) Closing segment.His Contact Info:TwitterWebsiteLinkedInGitHubMediumHis Recommended Resources:Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron CourvillePyImageSearch by Adrian RosebrockStation F Incubator in ParisBenevolentAIEconometrics Data Science: A Predictive Modeling Approach by Francis Diebold

Markov chains are a fairly common, and relatively simple, way to statistically model random processes. They have been used in many different domains, ranging from text generation to financial modeling. A popular example is r/SubredditSimulator, which uses Markov chains to automate the creation of content for an entire subreddit. Overall, Markov Chains are conceptually quite intuitive, and are very accessible in that they can be implemented without the use of any advanced statistical or mathematical concepts. They are a great way to start learning about probabilistic modeling and data science techniques. The Markov Chain is a model used to describe a sequence of consecutive events where the probability or chance of an event depends only on the event before it.If a sequence of events exhibits the Markov Property of the reliance on the previous state, then the sequence is called ‘Markovian’ in nature. For some problems in Reinforcement Learning, the actions performed in a particular state is directly related to the previous state, the actions performed in that state and the rewards that the agent receives upon performing said actions.We from BEPEC are ready to help you and make you shift your career at any cost.For more details visit: https://www.bepec.in/machinelearningcourseBepec registration form : https://www.bepec.in/registration-formCheck our youtube channel for more videos and please subscribe: https://www.youtube.com/channel/UCn1U...Check our Instagram page: https://instagram.com/bepec_solutions/Check our Facebook Page : https://www.facebook.com/Bepecsolutions/

Our guest Chris Hobbs, Sr. Software Engineer at Malwarebytes, gives us a glimpse into his world of security software and penetration testing. Tyrel receives a threatening voicemail generated by legacy voice generation technology.  Casey rambles on for half the episode about the origin of Django. http://friday.hirelofty.com/ https://facebook.com/fridaydeploy https://twitter.com/fridaydeploy Mentioned in this Episode Kitboga calls scammers on Twitch James Bennet's letter, "Core no more" Django Enhancement Proposalfor Core Team "Build a Slack bot that mimics your colleagues", Our blog post on Slack bots made with Markov Chains. AirBnb will start designing houses in 2019, Fast Company

Story: DEATH BY UMBRELLA: The Georgi Markov story. A tale of intrigue and covert operations from the late Cold War that John Le Carre couldn't have scripted any better.  Guest: Mike Konczal (@rortybomb), Fellow at the Roosevelt Institute and contributor to Vox, The Nation, Dissent, and other fine publications. Mike and I discuss the art of punditry, appearing on TV without pants, the myth of a democratized economy, and stock buybacks. We also do Professor Brothers voices. Performance by ANDREW BENTLEY. See below for information on Andrew's upcoming live performances in Chicago. Topic: Why we're living in the Golden Age of Gerrymandering. Hint: It's not just because Republicans are assholes. But it's definitely partly because Republicans are assholes. Cocktail of the Month: Lime Rickey (aka Gin Rickey, but we'll get into the complicated nomenclature soon enough)   Support Mass for Shut-ins via Patreon. Contact me via Facebook, Twitter (@gin_and_tacos), or the venerable website Gin and Tacos. Thanks: Mike Konczal, the bands that contribute music (Waxeater, IfIHadAHiFi, The Sump Pumps, Oscar Bait), Zachary Sielaff, Question Cathy, and all Patreon supporters, subscribers, and listeners. Hear more of Andrew Bentley on 3/31 at the American Writer's Museum as part of International Tom Hanks Day, at Write Club Chicago on 4/17, and on 4/7 at C2E2 in a musical comedy on the Cards Against Humanity stage.

Annie and Jon join us for a speculative discussion about the future of design. Will artificial intelligence and machine learning replace us in the future? How will we develop taste in automated systems? What will become of our industry when capitalism finally implodes? Links Discussed “Merlin Manndroid” AirBNB Design Artificial Intelligence Assembly Line Twitterbot Algorithm Pun Machine Learning Word Embedding Pupper2Vec: Analyzing Internet Dog Slang with Machine Learning Anthony Jeselnik Markov Chain Replika App Creativity Planet Money AirBNB — Painting with Code Material Design Image Synthesis From Text With Deep Learning Philosopher King Basic Income A/B Testing Zine Baby Boomers The arc of the universe is long… Dark Ages List of Countries by Infant Mortality Rate The Matrix Pokémon Red Programmed in Minecraft Making Music and Art Through Machine Learning Musical Novelty Search — Evolutionary Algorithms + Ableton Live Moonlight Upstream Color Dream Daddy SmarterChild Alexa Siri

In this talk, Jane presents about her work on modelling dynamic behaviour of systems using quantative modelling techniques. Particular kinds of modelling diagrams are used and a mathematical approach to looking at their meaning is presented.

At some point, statistical problems need sampling. Sampling consists in generating observations from a specific distribution.

Welcome to the 43rd Episode of Learning Machines 101!We are currently presenting a subsequence of episodes covering the events of the recent Neural Information Processing Systems Conference. However, this weekwill digress with a rerun of Episode 22 which nicely complements our previous discussion of the Monte Carlo Markov Chain Algorithm Tutorial. Specifically, today wediscuss the problem of approaches for learning or equivalently parameter estimation in Monte Carlo Markov Chain algorithms. The topics covered in this episode include: What is the pseudolikelihood method and what are its advantages and disadvantages?What is Monte Carlo Expectation Maximization? And...as a bonus prize...a mathematical theory of "dreaming"!!! The current plan is to returnto coverage of the Neural Information Processing Systems Conference in 2 weeks on January 25!! Check out: www.learningmachines101.com for more details!

This is the second of a short subsequence of podcasts providing a summary of events associated with Dr. Golden’s recent visit to the 2015 Neural Information Processing Systems Conference. This is one of the top conferences in the field of Machine Learning. This episode reviews and discusses topics associated with the Monte Carlo Markov Chain (MCMC) Inference Methods Tutorial held on the first day of the conference. Check out: www.learningmachines101.com   to listen or download this podcast episode or download the transcripts! Also visit us at LINKEDIN or TWITTER. The twitter handle is: LM101TALK  

In this episode we discuss how to solve constraint satisfaction inference problems where knowledge is represented as a large unordered collection of complicated probabilistic constraints among a collection of variables. The goal of the inference process is to infer the most probable values of the unobservable variables given the observable variables. Concepts of Markov Random Fields and Monte Carlo Markov Chain methods are discussed. For additional details and technical notes, please visit the website: www.learningmachines101.com Also feel free to visit us at twitter: @lm101talk  

In this lecture, the professor discussed Markov process definition, n-step transition probabilities, and classification of states.

In this lecture, the professor discussed Markov process, steady-state behavior, and birth-death processes.

In this lecture, the professor discussed Markov Processes, probability of blocked phone calls, absorption probabilities, and calculating expected time to absorption.

In this lecture, the professor discussed Markov process definition, n-step transition probabilities, and classification of states.

In this lecture, the professor discussed Markov process, steady-state behavior, and birth-death processes.

In this lecture, the professor discussed Markov Processes, probability of blocked phone calls, absorption probabilities, and calculating expected time to absorption.

In this lecture, the professor covers sample-time M/M/1 queue, Burke’s theorem, branching processes, and Markov processes with countable state spaces.

The transition matrix approach to finite-state Markov chains is developed in this lecture. The powers of the transition matrix are analyzed to understand steady-state behavior. (Courtesy of Shan-Yuan Ho. Used with permission.)

This lecture begins with a discussion of convergence WP1 related to a quiz problem. Then positive and null recurrence, steady state, birth-death chains, and reversibility are covered.

This episode introduces the idea of a Markov Chain. A Markov Chain has a set of states describing a particular system, and a probability of moving from one state to another along every valid connected state. Markov Chains are memoryless, meaning they don't rely on a long history of previous observations. The current state of a system depends only on the previous state and the results of a random outcome. Markov Chains are a useful way method for describing non-deterministic systems. They are useful for destribing the state and transition model of a stochastic system. As examples of Markov Chains, we discuss stop light signals, bowling, and text prediction systems in light of whether or not they can be described with Markov Chains.

Ever feel like you could randomly assemble words from a certain vocabulary and make semi-coherent Kanye West lyrics? Or technical documentation, imitations of local newscasters, your politically outspoken uncle, etc.? Wonder no more, there's a way to do this exact type of thing: it's called a Markov Chain, and probably the most powerful way to generate made-up data that you can then use for fun and profit. The idea behind a Markov Chain is that you probabilistically generate a sequence of steps, numbers, words, etc. where each next step/number/word depends only on the previous one, which makes it fast and efficient to computationally generate. Usually Markov Chains are used for serious academic uses, but this ain't one of them: here they're used to randomly generate rap lyrics based on Kanye West lyrics.

We discuss how to solve constraint satisfaction inference problems where knowledge is represented as a large unordered collection of complicated probabilistic constraints among a collection of variables. The goal of the inference process is to infer the most probable values of the unobservable variables given the observable variables. Please visit: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!

After talking about what they've been doing at home, Andrew and Steve talk about Raspberry Pi, Broadwell, game development, the internet, Internet Explorer, OpenGL, USB, Markov Chains, and dice.

Speaker: Prof. J. J. Hunter Abstract: In a finite m-state irreducible Markov chain with stationary probabilities {pi_i} and mean first passage times m_{ij} (mean recurrence time when i=j) it was first shown, by Kemeny and Snell, that sum_{j=1}^{m}pi_jm_{ij} is a constant, K, not depending on i. This constant has since become known as Kemeny’s constant. We consider a variety of techniques for finding expressions for K, derive some bounds for K, and explore various applications and interpretations of theseresults. Interpretations include the expected number of links that a surfer on the World Wide Web located on a random page needs to follow before reaching a desired location, as well as the expected time to mixing in a Markov chain. Various applications have been considered including some perturbation results, mixing on directed graphs and its relation to the Kirchhoff index of regular graphs.

Speaker: Prof. J. J. Hunter Abstract: In a finite m-state irreducible Markov chain with stationary probabilities {pi_i} and mean first passage times m_{ij} (mean recurrence time when i=j) it was first shown, by Kemeny and Snell, that sum_{j=1}^{m}pi_jm_{ij} is a constant, K, not depending on i. This constant has since become known as Kemeny’s constant. We consider a variety of techniques for finding expressions for K, derive some bounds for K, and explore various applications and interpretations of theseresults. Interpretations include the expected number of links that a surfer on the World Wide Web located on a random page needs to follow before reaching a desired location, as well as the expected time to mixing in a Markov chain. Various applications have been considered including some perturbation results, mixing on directed graphs and its relation to the Kirchhoff index of regular graphs.

Speaker: Prof. S. Kirkland Abstract: A square matrix T is called stochastic if its entries are nonnegative and its row sums are all equal to one. Stochastic matrices are the centrepiece of the theory of discrete-time, time homogenous Markov chains on a finite state space. If some power of the stochastic matrix T has all positive entries, then there is a unique left eigenvector for T, known as the stationary distribution, to which the iterates of the Markov chain converge, regardless of what the initial distribution for the chain is. Thus, in this setting, the stationary distribution can be thought of as giving the probability that the chain is in a particular state over the long run. In many applications, the stochastic matrix under consideration is equipped with an underlying combinatorial structure, which can be recorded in a directed graph. Given a stochastic matrix T, how are the entries in the stationary distribution influenced by the structure of the directed graph associated with T? In this talk we investigate a question of that type by finding the minimum value of the maximum entry in the stationary distribution for T, as T ranges over the set of stochastic matrices with a given directed graph. The solution involves techniques from matrix theory, graph theory, and nonlinear programming.

Speaker: Prof. S. Kirkland Abstract: A square matrix T is called stochastic if its entries are nonnegative and its row sums are all equal to one. Stochastic matrices are the centrepiece of the theory of discrete-time, time homogenous Markov chains on a finite state space. If some power of the stochastic matrix T has all positive entries, then there is a unique left eigenvector for T, known as the stationary distribution, to which the iterates of the Markov chain converge, regardless of what the initial distribution for the chain is. Thus, in this setting, the stationary distribution can be thought of as giving the probability that the chain is in a particular state over the long run. In many applications, the stochastic matrix under consideration is equipped with an underlying combinatorial structure, which can be recorded in a directed graph. Given a stochastic matrix T, how are the entries in the stationary distribution influenced by the structure of the directed graph associated with T? In this talk we investigate a question of that type by finding the minimum value of the maximum entry in the stationary distribution for T, as T ranges over the set of stochastic matrices with a given directed graph. The solution involves techniques from matrix theory, graph theory, and nonlinear programming.

MIT OCW: 21M.380 Music and Technology: Algorithmic and Generative Music, Spring 2010

Finish the discussion of HMMs for CpG islands. Introduction to the Vitterbi algorithm (really dynamic programming) to find the most likely Markov Chain generating a given sequence.

van den Berg, J (CWI) Thursday 27 March 2008, 10:05-10:35 Markov-chain Monte Carlo Methods

Montenegro, R (Massachusetts Lowell) Wednesday 26 March 2008, 14:35-15:05 Markov-chain Monte Carlo Methods

Cameron, PJ (London) Tuesday 25 March 2008, 14:35-15:05 Markov-chain Monte Carlo Methods

Motivated by multivariate random recurrence equations we prove a new analogue of the Key Renewal Theorem for functionals of a Markov chain with compact state space in the spirit of Kesten. We simplify and modify Kesten's proof.