Learning Bayesian Statistics

Episode sponsored by Tidelift: tidelift.comImagine me rapping: "Let me show you how to be a good Bayesian. Change your predictions after taking information in, and if you’re thinking I’ll be less than amazing, let’s adjust those expectations!"What?? Nah, you’re right, I’m not as good as Baba Brinkman. Actually, the best to perform « Good Bayesian » live on the podcast would just be to invite him for an episode… Wait, isn’t that what I did???Well indeed! For this episode, I had the great pleasure of hosting rap artist, science communicator and revered author of « Good Bayesian », Baba Brinkman!We talked about his passion for oral poetry, his rap career, what being a good rapper means and the difficulties he encounters to establish himself as a proper rapper.Baba began his rap career in 1998, freestyling and writing songs in his hometown of Vancouver, Canada.In 2000 he started adapting Chaucer’s Canterbury Tales into original rap compositions, and in 2004 he premiered a one man show based on his Master’s thesis, The Rap Canterbury Tales, exploring parallels between hip-hop music and medieval poetry.Over the years, Baba went on to create “Rap Guides” dedicated to scientific topics, like evolution, consciousness, medicine, religion, and climate change – and I encourage you to give them all a listen!By the way, do you know the common point between rap and evolutionary biology? Well, you’ll have to tune in for the answer… And make sure you listen until the end: Baba has a very, very nice surprise for you!A little tip: if you wanna enjoy it to the fullest, I put the unedited video version of this interview in the show notes ;) By the way, let me know if you like these video live streams — I might just do them again if you do!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, 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, Jonathan Sedar, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski and Tim Radtke.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Video live-stream of the episode: https://www.youtube.com/watch?v=YkFXpP_SvHkBaba on Twitter: https://twitter.com/bababrinkmanBaba on YouTube: https://www.youtube.com/channel/UCz9Qm66ewnY0LAlZlL4HK9gBaba on Spotify: https://open.spotify.com/artist/7DqKchcLvOIgR87RzJm3XHBaba's website: https://bababrinkman.com/Event Rap Kickstarter: https://www.kickstarter.com/projects/bababrinkman/event-rap-the-one-stop-custom-rap-shopEvent Rap website: https://www.eventrap.com/Anil Seth -- Your Brain Hallucinates your Conscious Reality: https://www.ted.com/talks/anil_seth_your_brain_hallucinates_your_conscious_realityThe Big Picture -- On the Origins of Life, Meaning, and the Universe Itself: https://www.amazon.com/Big-Picture-Origins-Meaning-Universe/dp/1101984252

Episode sponsored by Tidelift: tidelift.comI don’t know about you, but the notion of time is really intriguing to me: it’s a purely artificial notion; we humans invented it — as an experiment, I asked my cat what time it was one day; needless to say it wasn’t very conclusive… And yet, the notion of time is so central to our lives — our work, leisures and projects depend on it.So much so that time series predictions represent a big part of the statistics and machine learning world. And to talk about all that, who better than a time master, namely Sean Taylor?Sean is a co-creator of the Prophet time series package, available in R and Python. He’s a social scientist and statistician specialized in methods for solving causal inference and business decision problems. Sean is particularly interested in building tools for practitioners working on real-world problems, and likes to hang out with people from many fields — computer scientists, economists, political scientists, statisticians, machine learning researchers, business school scholars — although I guess he does that remotely these days…Currently head of the Rideshare Labs team at Lyft, Sean was a research scientist and manager on Facebook’s Core Data Science Team and did a PhD in information systems at NYU’s Stern School of Business. He did his undergraduate at the University of Pennsylvania, studying economics, finance, and information systems. Last but not least, he grew up in Philadelphia, so, of course, he’s a huge Eagles fan! For my non US listeners, we’re talking about the football team here, not the bird!We also talked about two of my favorite topics — science communication and epistemology — so I had a lot of fun talking with Sean, and I hope you’ll deem this episode a good investment of your time Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, 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, Jonathan Sedar, Hugo Botha, Vinh Nguyen and Raul Maldonado.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Sean's website: https://seanjtaylor.com/Sean on GitHub: https://github.com/seanjtaylorSean on Twitter: https://twitter.com/seanjtaylorProphet docs: https://facebook.github.io/prophet/Forecasting at Scale -- How and why we developed Prophet for forecasting at Facebook: https://www.youtube.com/watch?v=OaTAe4W9IfA Forecasting at Scale paper: https://www.tandfonline.com/doi/abs/10.1080/00031305.2017.1380080?journalCode=utas20&TimeSeers -- Hierarchical version of Prophet, written in PyMC3: https://github.com/MBrouns/timeseersThe Art of Doing Science and Engineering -- Learning to Learn: https://www.amazon.com/Art-Doing-Science-Engineering-Learning/dp/1732265178NeuralProphet -- Forecasting model based on Neural Networks in PyTorch: https://github.com/ourownstory/neural_prophet/Introducing PyMC Labs: https://www.pymc-labs.io/blog-posts/saving-the-world/

Episode sponsored by Tidelift: tidelift.comI bet you already heard of Bayesian nonparametric models, at least on this very podcast. We already talked about Dirichlet Processes with Karin Knudson on episode 4, and then about Gaussian Processes with Elizaveta Semenova on episode 21. Now we’re gonna dive into the mathematical properties of these objects, to understand them better — because, as you may know, Bayesian nonparametrics are quite powerful but also very hard to fit!Along the way, you’ll learn about probabilistic circuits, sum-product networks and — what a delight — you’ll hear from the Julia community! Indeed, my guest for this episode is no other than… Martin Trapp!Martin is a core developer of Turing.jl, an open-source framework for probabilistic programming in Julia, and a post-doc in probabilistic machine learning at Aalto University, Finland.Martin loves working on sum-product networks and Bayesian non-parametrics. And indeed, his research interests focus on probabilistic models that exploit structural properties to allow efficient and exact computations while maintaining the capability to model complex relationships in data. In other words, Martin’s research is focused on tractable probabilistic models.Martin did his MsC in computational intelligence at the Vienna University of Technology and just finished his PhD in machine learning at the Graz University of Technology. He doesn’t only like to study the tractability of probabilistic models — he also is very found of climbing!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, 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, Jonathan Sedar, Hugo Botha, Vinh Nguyen and Raul Maldonado.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Martin's website: https://trappmartin.github.io/Martin on GitHub: https://github.com/trappmartinMartin on Twitter: https://twitter.com/martin_trappTuring, Bayesian inference with Julia: https://turing.ml/dev/Hierarchical Dirichlet Processes: https://people.eecs.berkeley.edu/~jordan/papers/hdp.pdfThe Automatic Statistician: https://www.doc.ic.ac.uk/~mpd37/teaching/2014/ml_tutorials/2014-01-29-slides_zoubin2.pdfTruncated Random Measures: https://arxiv.org/abs/1603.00861Deep Structured Mixtures of Gaussian Processes: https://arxiv.org/abs/1910.04536Probabilistic Circuits -- Representations, Inference, Learning and Theory: https://www.youtube.com/watch?v=2RAG5-L9R70Applied Stochastic Differential Equations, from Simo Särkkä and Arno Solin: https://users.aalto.fi/~asolin/sde-book/sde-book.pdf

Episode sponsored by Tidelift: tidelift.comOne of the most common guest suggestions that you dear listeners make is… inviting Paul Bürkner on the show! Why? Because he’s a member of the Stan development team and he created BRMS, a popular R package to make and sample from Bayesian regression models using Stan. And, as I like you, I did invite Paul on the show and, well, that was a good call: we had an amazing conversation, spanning so many topics that I can’t list them all here!I asked him why he created BRMS, in which fields it’s mostly used, what its weaknesses are, and which improvements to the package he’s currently working on. But that’s not it! Paul also gave his advice to people realizing that Bayesian methods would be useful to their research, but who fear facing challenges from advisors or reviewers.Besides being a Bayesian rockstar, Paul is a statistician working as an Independent Junior Research Group Leader at the Cluster of Excellence SimTech at the University of Stuttgart, Germany. Previously, he has studied Psychology and Mathematics at the Universities of Münster and Hagen and did his PhD in Münster about optimal design and Bayesian data analysis, and he also worked as a Postdoctoral researcher at the Department of Computer Science at Aalto University, Finland.So, of course, I asked him about the software-assisted Bayesian workflow that he’s currently working on with Aki Vehtari, which led us to no less than the future of probabilistic programming languages…Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, 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 and Jonathan Sedar.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Paul's website: https://paul-buerkner.github.io/about/Paul on Twitter: https://twitter.com/paulbuerknerPaul on GitHub: https://github.com/paul-buerknerBRMS docs: https://paul-buerkner.github.io/brms/Stan docs: https://mc-stan.org/Bayesian workflow paper: https://arxiv.org/pdf/2011.01808v1.pdf

Lauren Kennedy, a Business Analytics lecturer at Monash University, discusses the complexities of multilevel regression and post-stratification (MRP) for analyzing non-representative data. She shares insights on how structured priors can enhance demographic analysis, addresses the challenges of missing data imputation, and highlights the importance of causal inference in social sciences. Additionally, Kennedy emphasizes teaching Bayesian methods through practical workflows, ethical considerations in data analytics, and the necessity for inclusivity in statistical research.

How do people choose their career? How do they change jobs? How do they even change careers? These are important questions that we don’t have great answers to. But structured data about the dynamics of labor markets are starting to emerge, and that’s what Ben Zweig is modeling at Revelio Labs.An economist and data scientist, Ben is indeed the CEO of Revelio Labs, a data science company analyzing raw labor data contained in resumes, online profiles and job postings. In this episode, he’ll tell us about the Bayesian structural time series model they built to estimate inflows and outflows from companies, using LinkedIn data — a very challenging but fascinating endeavor, as you’ll hear!As a lot of people, Ben has always used more traditional statistical models but had been intrigued by Bayesian methods for a long time. When they started working on this Bayesian time series model though, he had to learn a bunch of new methods really quickly. I think you’ll find interesting to hear how it went…Ben also teaches data science and econometrics at the NYU Stern school of business, so he’ll reflect on his experience teaching Bayesian methods to economics students. Prior to that, Ben did a PhD in economics at the City University of New York, and has done research in occupational transformation and social mobility.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, 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 and Rémi Louf.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Ben's bio: https://www.stern.nyu.edu/faculty/bio/benjamin-zweigRevelio Labs blog: https://www.reveliolabs.com/blog/Predicting the Present with Bayesian Structural Time Series: https://people.ischool.berkeley.edu/~hal/Papers/2013/pred-present-with-bsts.pdfA Hierarchical Framework for CorrectingUnder-Reporting in Count Data: https://arxiv.org/pdf/1809.00544.pdfTensorFlow Probability module for Bayesian structural time series models: https://www.tensorflow.org/probability/api_docs/python/tfp/sts/ Fitting Bayesian structural time series with the bsts R package: https://www.unofficialgoogledatascience.com/2017/07/fitting-bayesian-structural-time-series.htmlCausalImpact, an R package for causal inference using Bayesian structural time-series models: https://cran.r-project.org/web/packages/CausalImpact/vignettes/CausalImpact.html

When explaining Bayesian statistics to people who don’t know anything about stats, I often say that MCMC is about walking many different paths in lots of parallel universes, and then counting what happened in all these universes.And in a sense, this whole podcast is dedicated to sampling the whole distribution of Bayesian practitioners. So, for this episode, I thought we’d take a break of pure, hard modeling and talk about how to get involved into Bayesian statistics and open-source development, how companies use Bayesian tools, and what common struggles and misperceptions the latter suffer from.Quite the program, right? The good news is that Peadar Coyle, my guest for this episode, has done all of that! Coming to us from Armagh, Ireland, Peadar is a fellow PyMC core developer and was a data science and data engineer consultant until recently – a period during which he has covered all of modern startup data science, from AB testing to dashboards to data engineering to putting models into production.From these experiences, Peadar has written a book consisting of numerous interviews with data scientists throughout the world – and do consider buying it, as money goes to the NumFOCUS organization, under which many Bayesian stats packages live, like Stan, ArviZ, PyMC, etc.Now living in London, Peadar recently founded the start-up Aflorithmic, an AI solution that aims at developing personalized voice-first solutions for brands and enterprises. Their technology is also used to support children, families and elderly coping with the mental health challenges of COVID-19 confinements.Before all that, Peadar studied physics, philosophy and mathematics at the universities of Bristol and Luxembourg. When he’s away from keyboard, he enjoys the outdoors, cooking and, of course, watching rugby!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, 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 and Nathaniel Burbank.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:"Matchmaking Dinner" announcement: https://twitter.com/alex_andorra/status/1351136756087734272How to get acces to "Matchmaking Dinner" episodes: https://www.patreon.com/learnbayesstatsPeadar on Twitter: https://twitter.com/springcoilPeadar's website: https://peadarcoyle.com/Peadar on GitHub: https://github.com/springcoilInterviews with Data Scientists -- A discussion of the Industy and the current trends: https://leanpub.com/interviewswithdatascientists/Aflorithmic -- Personalized Audio SaaS Platform: https://www.aflorithmic.ai/Peadar's PyMC3 Primer: https://product.peadarcoyle.com/

I don’t know if you noticed, but I have a fondness for any topic related to decision-making under uncertainty — when it’s studied scientifically of course. Understanding how and why people make decisions when they don’t have all the facts is fascinating to me. That’s why I like electoral forecasting and I love cognitive sciences.So, for the first episode of 2021, I have a special treat: I had the great pleasure of hosting Michael Lee on the podcast! Yes, the Michael Lee who co-authored the book Bayesian Cognitive Modeling with Eric-Jan Wagenmakers in 2013 — by the way, the book was ported to PyMC3, I put the link in the show notes ;)This book was inspired from Michael’s work as a professor of cognitive sciences at University of California, Irvine. He works a lot on representation, memory, learning, and decision making, with a special focus on individual differences and collective cognition.Using naturally occurring behavioral data, he builds probabilistic generative models to try and answer hard real-world questions: how does memory impairment work (that’s modeled with multinomial processing trees)? How complex are simple decisions, and how do people change strategies?Echoing episode 18 with Daniel Lakens, Michael and I also talked about the reproducibility crisis: how are cognitive sciences doing, which progress was made, and what is still to do?Living now in California, Michael is originally from Australia, where he did his Bachelors of Psychology and Mathematics, and his PhD in psychology. But Michael is also found of the city of Amsterdam, which he sees as “the perfect antidote to southern California with old buildings, public transport, great bread and beer, and crappy weather”.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, 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 and Nathaniel Burbank.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Michael's website: https://faculty.sites.uci.edu/mdlee/Michael on GitHub: https://twitter.com/mdlBayesBayesian Cognitive Modeling book: https://faculty.sites.uci.edu/mdlee/bgm/Bayesian Cognitive Modeling in PyMC3: https://github.com/pymc-devs/resources/tree/master/BCMAn application of multinomial processing tree models and Bayesian methods to understanding memory impairment: https://drive.google.com/file/d/1NHml_YUsnpbUaqFhu0h8EiLeJCx6q403/viewUnderstanding the Complexity of Simple Decisions -- Modeling Multiple Behaviors and Switching Strategies: https://webfiles.uci.edu/mdlee/LeeGluckWalsh2018.pdfRobust Modeling in Cognitive Science: https://link.springer.com/article/10.1007/s42113-019-00029-y

It’s funny how powerful symbols are, right? The Eiffel Tower makes you think of Paris, the Statue of Liberty is New-York, and the Trevi Fountain… is Rome of course! Just with one symbol, you can invoke multiple concepts and ideas.You probably know that symbols are omnipresent in mathematics — but did you know that they are also very important in statistics, especially probabilistic programming?Rest assured, I didn’t really know either… until I talked with Brandon Willard! Brandon is indeed a big proponent of relational programming and symbolic computation, and he often promotes their use in research and industry. Actually, a few weeks after our recording, Brandon started spearheading the revival of Theano through the JAX backend that we’re currently working on for the future version of PyMC3!As you guessed it, Brandon is a core developer of PyMC, and also a contributor to Airflow and IPython, just to name a few. His interests revolve around the means and methods of mathematical modeling and its automation. In a nutshell, he’s a Bayesian statistician: he likes to use the language and logic of probability to quantify uncertainty and frame problems.After a Bachelor’s in physics and mathematics, Brandon got a Master’s degree in statistics from the University of Chicago. He’s worked in different areas in his career – from finance, transportation and energy to start-ups, gov-tech and academia. Brandon particularly loves projects where popular statistical libraries are inadequate, where sophisticated models must be combined in non-trivial ways, or when you have to deal with high-dimensional and discrete processes.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, 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 and Colin Carroll.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Brandon's website: https://brandonwillard.github.io/Brandon on GitHub: https://github.com/brandonwillardThe Future of PyMC3, or "Theano is Dead, Long Live Theano": https://pymc-devs.medium.com/the-future-of-pymc3-or-theano-is-dead-long-live-theano-d8005f8a0e9bNew Theano-PyMC library: https://github.com/pymc-devs/Theano-PyMCSymbolic PyMC: https://pymc-devs.github.io/symbolic-pymc/A Role for Symbolic Computation in the General Estimation of Statistical Models: https://brandonwillard.github.io/a-role-for-symbolic-computation-in-the-general-estimation-of-statistical-models.htmlSymbolic Math in PyMC3: https://brandonwillard.github.io/symbolic-math-in-pymc3.htmlDynamic Linear Models in Theano: https://brandonwillard.github.io/dynamic-linear-models-in-theano.htmlSymbolic PyMC Radon Example in PyMC4: https://brandonwillard.github.io/symbolic-pymc-radon-example-in-pymc4.html What I Wish Someone Had Told Me About Tensor Computation Libraries: https://eigenfoo.xyz/tensor-computation-libraries/

Associate professor Aki Vehtari discusses model assessment, non-parametric models like Gaussian processes, and Bayesian probability theory. He talks about teaching Bayesian statistics, open-source software development, and his work on a software-assisted Bayesian workflow. Vehtari shares insights on overcoming challenges in data analysis, model selection, and the dynamic nature of research in the field.