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Learning Bayesian Statistics

Author: Alexandre ANDORRA

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Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is?

Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow.

When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible.

So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped.

But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners!

My name is Alex Andorra by the way, and I live in Paris. By day, I'm a data scientist and modeler at the PyMC Labs consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. I also love election forecasting and, most importantly, Nutella. But I don't like talking about it – I prefer eating it.

So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and unlock exclusive Bayesian swag on Patreon!

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47 Episodes
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Episode sponsored by Paperpile: https://paperpile.com/ (paperpile.com) Get 20% off until December 31st with promo code GOODBAYESIAN21 Bonjour my dear Bayesians! Yes, it was bound to happen one day — and this day has finally come. Here is the first ever 100% French speaking ‘Learn Bayes Stats’ episode! Who is to blame, you ask? Well, who better than Rémi Louf? Rémi currently works as a senior data scientist at Ampersand, a big media marketing company in the US. He is the author and maintainer of several open source libraries, including MCX and BlackJAX. He holds a PhD in statistical Physics, a Masters in physics from the Ecole Normale Supérieure and a Masters in Philosophy from Oxford University. I think I know what you’re wondering: how the hell do you go from physics to philosophy to Bayesian stats?? Glad you asked, as it was my first question to Rémi! He’ll also tell us why he created MXC and BlackJax, what his main challenges are when working on open-source projects, and what the future of PPLs looks like to him. 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, 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, 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, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin and Philippe Labonde. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Rémi on GitHub: https://github.com/rlouf (https://github.com/rlouf) Rémi on Twitter: https://twitter.com/remilouf (https://twitter.com/remilouf) Rémi's website: https://rlouf.github.io/ (https://rlouf.github.io/) BlackJAX -- Fast & modular sampling library: https://github.com/blackjax-devs/blackjax (https://github.com/blackjax-devs/blackjax) MCX -- Probabilistic programs on CPU & GPU, powered by JAX: https://github.com/rlouf/mcx (https://github.com/rlouf/mcx) aeppl, Tools for a PPL in Aesara: https://github.com/aesara-devs/aeppl (https://github.com/aesara-devs/aeppl) French Presidents' popularity dashboard: https://www.pollsposition.com/popularity (https://www.pollsposition.com/popularity) How to model presidential approval (in French): https://anchor.fm/pollspolitics/episodes/10-Comment-Modliser-la-Popularit-e121jh2 (https://anchor.fm/pollspolitics/episodes/10-Comment-Modliser-la-Popularit-e121jh2) LBS #23, Bayesian Stats in Business & Marketing, with Elea McDonnel Feit: https://www.learnbayesstats.com/episode/23-bayesian-stats-in-business-and-marketing-analytics-with-elea-mcdonnel-feit (https://www.learnbayesstats.com/episode/23-bayesian-stats-in-business-and-marketing-analytics-with-elea-mcdonnel-feit) LBS #30, Symbolic Computation & Dynamic Linear Models, with Brandon Willard: https://www.learnbayesstats.com/episode/symbolic-computation-dynamic-linear-models-brandon-willard (https://www.learnbayesstats.com/episode/symbolic-computation-dynamic-linear-models-brandon-willard) This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast
Episode sponsored by Paperpile: https://paperpile.com/ (paperpile.com) Get 20% off until December 31st with promo code GOODBAYESIAN21 I don’t know if you’ve heard, but there is a virus that took over most of the world in the past year? I haven’t dedicated any episode to Covid yet. First because research was moving a lot — and fast. And second because modeling Covid is very, very hard. But we know more about it now, so I thought it was a good time to pause and ponder — how does the virus circulate? How can we model it and, ultimately, defeat it? What are the challenges in doing so? To talk about that, I had the chance to host Michael Osthege and Thomas Vladeck, who both were part of the team who developed the Rt-live model, a Bayesian model to infer the reproductive rate of Covid19 in the general population. As you’ll hear, modeling the evolution of this virus is challenging, fascinating, and a perfect fit for Bayesian modeling! It truly is a wonderful example of Bayesian generative modeling. Tom is the Managing Director of Gradient Metrics, a quantitative market research firm, and a Co-Founder of Recast, a media mix model for modern brands. Michael is a PhD student in laboratory automation and bioprocess optimization at the Forschungszentrum Jülich in Germany, and a fellow PyMC core-developer. As he works a lot on the coming brand new version 4, we’ll take this opportunity to talk about the current developments and where the project is headed. 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, 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, 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, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode and Patrick Kelley. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Tom on Twitter: https://twitter.com/tvladeck (https://twitter.com/tvladeck) Tom's newsletter: https://tvladeck.substack.com/ (https://tvladeck.substack.com/) Michael on Twitter: https://twitter.com/theCake (https://twitter.com/theCake) Michael on GitHub: https://github.com/michaelosthege (https://github.com/michaelosthege) Rt Live dashboard: https://rtlive.de/global.html (https://rtlive.de/global.html) Rt Live model tutorial: https://github.com/rtcovidlive/rtlive-global/blob/master/notebooks/Tutorial_model.ipynb (https://github.com/rtcovidlive/rtlive-global/blob/master/notebooks/Tutorial_model.ipynb) Rt Live model code: https://github.com/rtcovidlive/rtlive-global (https://github.com/rtcovidlive/rtlive-global) Estimating Rt: https://staff.math.su.se/hoehle/blog/2020/04/15/effectiveR0.html (https://staff.math.su.se/hoehle/blog/2020/04/15/effectiveR0.html) Great resource on terminology: https://royalsociety.org/-/media/policy/projects/set-c/set-covid-19-R-estimates.pdf?la=en-GB&hash=FDFFC11968E5D247D8FF641930680BD6 (https://royalsociety.org/-/media/policy/projects/set-c/set-covid-19-R-estimates.pdf?la=en-GB&hash=FDFFC11968E5D247D8FF641930680BD6) Using Hierarchical Multinomial Regression to Predict Elections in Paris districts: https://www.youtube.com/watch?v=EYdIzSYwbSw... Support this podcast
Episode sponsored by Paperpile: https://paperpile.com/ (paperpile.com) Get 20% off until December 31st with promo code GOODBAYESIAN21 We often talk about applying Bayesian statistics on this podcast. But how do we teach them? What’s the best way to introduce them from a young age and make sure the skills students learn in the stats class are transferable? Well, lucky us, Mine Dogucu’s research tackles precisely those topics! An Assistant Professor of Teaching in the Department of Statistics at University of California Irvine, Mine is both an educator with an interest in statistics, and an applied statistician with experience in educational research. Her work focuses on modern pedagogical approaches in the statistics curriculum, making data science education more accessible. In particular, she teaches an undergraduate Bayesian course, and is the coauthor of the upcoming book Bayes Rules! An Introduction to Bayesian Modeling with R. In other words, Mine is not only interested in teaching, but also in how best to teach statistics – how to engage students in remote classes, how to get to know them, how to best record and edit remote courses, etc. She writes about these topics on her blog, DataPedagogy.com. She also works on accessibility and inclusion, as well as a study that investigates how popular Bayesian courses are at the undergraduate level in the US — that should be fun to talk about! Mine did her Master’s at Bogazici University in Istanbul, Turkey, and then her PhD in Quantitative Research, Evaluation, and Measurement at Ohio State University. 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, 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, 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, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, John Johnson, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode and Patrick Kelley. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Mine's website: https://mdogucu.ics.uci.edu/index.html (https://mdogucu.ics.uci.edu/index.html) Mine's blog: https://www.datapedagogy.com/ (https://www.datapedagogy.com/) Mine on Twitter: https://twitter.com/MineDogucu (https://twitter.com/MineDogucu) Mine on GitHub: https://github.com/mdogucu (https://github.com/mdogucu) Bayes Rules! An Introduction to Bayesian Modeling with R: https://www.bayesrulesbook.com/ (https://www.bayesrulesbook.com/) R package for Supplemental Materials for the Bayes Rules! Book: https://github.com/bayes-rules/bayesrules (https://github.com/bayes-rules/bayesrules) Stats 115 - Introduction to Bayesian Data Analysis: https://www.stats115.com/ (https://www.stats115.com/) Undergraduate Bayesian Education Network: https://undergrad-bayes.netlify.app/network.html (https://undergrad-bayes.netlify.app/network.html) Workshop "Teaching Bayesian Statistics at the Undergraduate Level": https://www.causeweb.org/cause/uscots/uscots21/workshop/4 (https://www.causeweb.org/cause/uscots/uscots21/workshop/4) This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast
Let’s think Bayes, shall we? And who better to do that than the author of the well known book, Think Bayes — Allen Downey himself! Since the second edition was just released, the timing couldn’t be better! Allen is a professor at Olin College and the author of books related to software and data science, including Think Python, Think Bayes, and Think Complexity. His blog, Probably Overthinking It, features articles on Bayesian probability and statistics. He holds a Ph.D. from U.C. Berkeley, and bachelors and masters degrees from MIT. In this special episode, Allen and I talked about his background, how he came to the stats and teaching worlds, and why he wanted to write this book in the first place. He’ll tell us who this book is written for, what’s new in the second edition, and which mistakes his students most commonly make when starting to learn Bayesian stats. We also talked about some types of models, their usefulness and their weaknesses, but I’ll let you discover that. Now for another good news: 5 Patrons of the show will get Think Bayes for free! To qualify, you just need to go the form I linked to in the 'Learn Bayes Stats' Slack channel or https://www.patreon.com/learnbayesstats (the Patreon page) and enter your email address. That’s it. After a week or so, Allen and I will choose 5 winners at random, who will receive the book for free! If you’re not a Patron yet, make sure to check out https://www.patreon.com/learnbayesstats (patreon.com/learnbayesstats) if you don’t want to miss out on these goodies! And even if you’re not a Patron, I love you dear listeners, so you all get a discount when you go buy the book at https://www.learnbayesstats.com/buy-think-bayes (https://www.learnbayesstats.com/buy-think-bayes) (unfortunately, this only applies for purchases in the US and Canada). 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, 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, 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, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, John Johnson and Hector Munoz. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Give LBS a 5-star rating on Podchaser: https://www.podchaser.com/learnbayesstats (https://www.podchaser.com/learnbayesstats) Buy Think Bayes at a 40% discount with the code LBS40 (expires on July 31; only applies for purchases in the US and Canada): https://www.learnbayesstats.com/buy-think-bayes (https://www.learnbayesstats.com/buy-think-bayes) Think Bayes 2 online: http://allendowney.github.io/ThinkBayes2/index.html (http://allendowney.github.io/ThinkBayes2/index.html) Allen's blog: https://www.allendowney.com/blog/ (https://www.allendowney.com/blog/) Allen on Twitter: https://twitter.com/allendowney (https://twitter.com/allendowney) Allen on GitHub: https://github.com/AllenDowney (https://github.com/AllenDowney) Information theory, inference and learning algorithms, David MacKay: https://www.inference.org.uk/itila/ (https://www.inference.org.uk/itila/) Statistical Rethinking, Richard McElreath: http://xcelab.net/rm/statistical-rethinking/ (http://xcelab.net/rm/statistical-rethinking/) Support this podcast
We all know about these accidental discoveries — penicillin, the heating power of microwaves, or the famous (and delicious) tarte tatin. I don’t know why, but I just love serendipity. And, as you’ll hear, this episode is deliciously full of it… Thanks to Allison Hilger and Timo Roettger, we’ll discover the world of linguistics, how Bayesian stats are helpful there, and how Paul Bürkner’s BRMS package has been instrumental in this field. To my surprise — and perhaps yours — the speech and language sciences are pretty quantitative and computational! As she recently discovered Bayesian stats, Allison will also tell us about the challenges she’s faced from advisors and reviewers during her PhD at Northwestern University, and the advice she’d have for people in the same situation. Allison is now an Assistant Professor at the University of Colorado Boulder. The overall goal in her research is to improve our understanding of motor speech control processes, in order to inform effective speech therapy treatments for improved speech naturalness and intelligibility. Allison also worked clinically as a speech-language pathologist in Chicago for a year. As a new Colorado resident, her new hobbies include hiking, skiing, and biking — and then reading or going to dog parks when she’s to tired. Holding a PhD in linguistics from the University of Cologne, Germany, Timo is an Associate Professor for linguistics at the University of Oslo, Norway. Timo tries to understand how people communicate their intentions using speech – how are speech signals retrieved; how do people learn and generalize? Timo is also committed to improving methodologies across the language sciences in light of the replication crisis, with a strong emphasis on open science. Most importantly, Timo loves hiking, watching movies or, even better, watching people play video games! 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, 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, 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, Tim Radtke, Adam C. Smith, Will Kurt and Andrew Moskowitz. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Allison's website: https://allisonhilger.com/ (https://allisonhilger.com/) Allison on Twitter: https://twitter.com/drahilger (https://twitter.com/drahilger) Allison's motor speech lab: https://www.colorado.edu/lab/motor-speech/ (https://www.colorado.edu/lab/motor-speech/) Timo's website: https://www.simplpoints.com/ (https://www.simplpoints.com/) Timo on Twitter: https://twitter.com/TimoRoettger (https://twitter.com/TimoRoettger) Bayesian regression modeling (for factorial designs) -- A tutorial: https://psyarxiv.com/cdxv3 (https://psyarxiv.com/cdxv3) An Introduction to Bayesian Multilevel Models Using brms -- A Case Study of Gender Effects on Vowel Variability in Standard Indonesian: https://biblio.ugent.be/publication/8624552/file/8624553.pdf (https://biblio.ugent.be/publication/8624552/file/8624553.pdf) Longitudinal Growth in Intelligibility of Connected Speech From 2 to 8 Years in Children With Cerebral Palsy -- A Novel Bayesian Approach:... Support this podcast
Episode sponsored by Tidelift: https://tidelift.com/ (tidelift.com) It’s been a while since we talked about biostatistics and bioinformatics on this podcast, so I thought it could be interesting to talk to Jacki Buros — and that was a very good idea! She’ll walk us through examples of Bayesian models she uses to, for instance, work on biomarker discovery for cancer immunotherapies. She’ll also introduce you to survival models — their usefulness, their powers and their challenges. Interestingly, all of this will highlight a handful of skills that Jacki would try to instill in her students if she had to teach Bayesian methods. The Head of Data and Analytics at Generable, a state-of-the-art Bayesian platform for oncology clinical trials, Jacki has been working in biostatistics and bioinformatics for over 15 years. She started in cardiology research at the TIMI Study Group at Harvard Medical School before working in Alzheimer’s Disease genetics at Boston University and in biomarker discovery for cancer immunotherapies at the Hammer Lab. Most recently she was the Lead Biostatistician at the Institute for Next Generation Health Care at Mount Sinai. An open-source enthusiast, Jacki is also a contributor to Stan and rstanarm, and the author of the survivalstan package, a library of Stan models for survival analysis. Last but not least, Jacki is an avid sailor and skier! 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, 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, 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, Tim Radtke, Adam C. Smith, Will Kurt and Andrew Moskowitz. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Nominate "Learn Bayes Stats" as "Best Podcast of 2021" and "Best Tech Podcast" by entering its https://www.learnbayesstats.com/apple (Apple feed) in https://docs.google.com/forms/d/e/1FAIpQLSe60AOZu0FRvlX3GgLS1Ff8ztPgeJhVHTDhGNaTF3OLgA1Rxw/viewform (this form)! Jacki on Twitter: https://twitter.com/jackiburos (https://twitter.com/jackiburos) Jacki on GitHub: https://github.com/jburos (https://github.com/jburos) Jacki on Orcid: https://orcid.org/0000-0001-9588-4889 (https://orcid.org/0000-0001-9588-4889) survivalstan -- Survival Models in Stan: https://github.com/hammerlab/survivalstan (https://github.com/hammerlab/survivalstan) rstanarm -- R model-fitting functions using Stan: http://mc-stan.org/rstanarm/ (http://mc-stan.org/rstanarm/) Generable -- Bayesian platform for oncology clinical trials: https://www.generable.com/ (https://www.generable.com/) StanCon 2020 ArviZ presentation : https://github.com/arviz-devs/arviz_misc/tree/master/stancon_2020 (https://github.com/arviz-devs/arviz_misc/tree/master/stancon_2020) Thinking in Bets -- Making Smarter Decisions When You Don't Have All the Facts : https://www.goodreads.com/book/show/35957157-thinking-in-bets (https://www.goodreads.com/book/show/35957157-thinking-in-bets) Scott Kelly and his space travels (in French): https://www.franceculture.fr/emissions/la-methode-scientifique/la-methode-scientifique-mardi-30-janvier-2018... Support this podcast
Episode sponsored by Tidelift: https://tidelift.com/ (tidelift.com) Imagine 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/ (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 (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_SvHk (https://www.youtube.com/watch?v=YkFXpP_SvHk) Baba on Twitter: https://twitter.com/bababrinkman (https://twitter.com/bababrinkman) Baba on YouTube: https://www.youtube.com/channel/UCz9Qm66ewnY0LAlZlL4HK9g (https://www.youtube.com/channel/UCz9Qm66ewnY0LAlZlL4HK9g) Baba on Spotify: https://open.spotify.com/artist/7DqKchcLvOIgR87RzJm3XH (https://open.spotify.com/artist/7DqKchcLvOIgR87RzJm3XH) Baba's website: https://bababrinkman.com/ (https://bababrinkman.com/) Event Rap Kickstarter: https://www.kickstarter.com/projects/bababrinkman/event-rap-the-one-stop-custom-rap-shop (https://www.kickstarter.com/projects/bababrinkman/event-rap-the-one-stop-custom-rap-shop) Event Rap website: https://www.eventrap.com/ (https://www.eventrap.com/) Anil Seth -- Your Brain Hallucinates your Conscious Reality: https://www.ted.com/talks/anil_seth_your_brain_hallucinates_your_conscious_reality (https://www.ted.com/talks/anil_seth_your_brain_hallucinates_your_conscious_reality) The Big... Support this podcast
Episode sponsored by Tidelift: https://tidelift.com/ (tidelift.com) I 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/ (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 (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Sean's website: https://seanjtaylor.com/ (https://seanjtaylor.com/) Sean on GitHub: https://github.com/seanjtaylor (https://github.com/seanjtaylor) Sean on Twitter: https://twitter.com/seanjtaylor (https://twitter.com/seanjtaylor) Prophet docs: https://facebook.github.io/prophet/ (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 (https://www.youtube.com/watch?v=OaTAe4W9IfA)  Forecasting at Scale paper: https://www.tandfonline.com/doi/abs/10.1080/00031305.2017.1380080?journalCode=utas20& (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/timeseers (https://github.com/MBrouns/timeseers) The Art of Doing Science and Engineering -- Learning to Learn: https://www.amazon.com/Art-Doing-Science-Engineering-Learning/dp/1732265178 (https://www.amazon.com/Art-Doing-Science-Engineering-Learning/dp/1732265178) NeuralProphet --... Support this podcast
Episode sponsored by Tidelift: https://tidelift.com/ (tidelift.com) I 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/ (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 (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Martin's website: https://trappmartin.github.io/ (https://trappmartin.github.io/) Martin on GitHub: https://github.com/trappmartin (https://github.com/trappmartin) Martin on Twitter: https://twitter.com/martin_trapp (https://twitter.com/martin_trapp) Turing, Bayesian inference with Julia: https://turing.ml/dev/ (https://turing.ml/dev/) Hierarchical Dirichlet Processes: https://people.eecs.berkeley.edu/~jordan/papers/hdp.pdf (https://people.eecs.berkeley.edu/~jordan/papers/hdp.pdf) The Automatic Statistician: https://www.doc.ic.ac.uk/~mpd37/teaching/2014/ml_tutorials/2014-01-29-slides_zoubin2.pdf (https://www.doc.ic.ac.uk/~mpd37/teaching/2014/ml_tutorials/2014-01-29-slides_zoubin2.pdf) Truncated Random Measures: https://arxiv.org/abs/1603.00861 (https://arxiv.org/abs/1603.00861) Deep Structured Mixtures of Gaussian Processes: https://arxiv.org/abs/1910.04536 (https://arxiv.org/abs/1910.04536) Probabilistic Circuits -- Representations, Inference, Learning and Theory: https://www.youtube.com/watch?v=2RAG5-L9R70 (https://www.youtube.com/watch?v=2RAG5-L9R70) Applied Stochastic Differential Equations, from Simo Särkkä and Arno Solin: https://users.aalto.fi/~asolin/sde-book/sde-book.pdf (https://users.aalto.fi/~asolin/sde-book/sde-book.pdf) This podcast uses the following third-party services for analysis: Podcorn -... Support this podcast
Episode sponsored by Tidelift: https://tidelift.com/ (tidelift.com) One 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/ (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 (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Paul's website: https://paul-buerkner.github.io/about/ (https://paul-buerkner.github.io/about/) Paul on Twitter: https://twitter.com/paulbuerkner (https://twitter.com/paulbuerkner) Paul on GitHub: https://github.com/paul-buerkner (https://github.com/paul-buerkner) BRMS docs: https://paul-buerkner.github.io/brms/ (https://paul-buerkner.github.io/brms/) Stan docs: https://mc-stan.org/ (https://mc-stan.org/) Bayesian workflow paper: https://arxiv.org/pdf/2011.01808v1.pdf (https://arxiv.org/pdf/2011.01808v1.pdf) This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast
Episode sponsored by Tidelift: https://tidelift.com/ (tidelift.com) We already mentioned multilevel regression and post-stratification (MRP, or Mister P) on this podcast, but we didn’t dedicate a full episode to explaining how it works, why it’s useful to deal with non-representative data, and what its limits are. Well, let’s do that now, shall we? To that end, I had the delight to talk with Lauren Kennedy! Lauren is a lecturer in Business Analytics at Monash University in Melbourne, Australia, where she develops new statistical methods to analyze social science data. Working mainly with R and Stan, Lauren studies non-representative data, multilevel modeling, post-stratification, causal inference, and, more generally, how to make inferences from the social sciences. Needless to say that I asked her everything I could about MRP, including how to choose priors, why her recent paper about structured priors can improve MRP, and when MRP is not useful. We also talked about missing data imputation, and how all these methods relate to causal inference in the social sciences. If you want a bit of background, Lauren did her Undergraduates in Psychological Sciences and Maths and Computer Sciences at Adelaide University, with Danielle Navarro and Andrew Perfors, and then did her PhD with the same advisors. She spent 3 years in NYC with Andrew Gelman’s Lab at Columbia University, and then moved back to Melbourne in 2020. Most importantly, Lauren is an adept of crochet — she’s already on her third blanket! 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, 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 (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Lauren's website: https://jazzystats.com/ (https://jazzystats.com/) Lauren on Twitter: https://twitter.com/jazzystats (https://twitter.com/jazzystats) Lauren on GitHub: https://github.com/lauken13 (https://github.com/lauken13) Improving multilevel regression and poststratification with structured priors: https://arxiv.org/abs/1908.06716 (https://arxiv.org/abs/1908.06716) Using model-based regression and poststratification to generalize findings beyond the observed sample: https://arxiv.org/abs/1906.11323 (https://arxiv.org/abs/1906.11323) Lauren's beginners Bayes workshop: https://github.com/lauken13/Beginners_Bayes_Workshop (https://github.com/lauken13/Beginners_Bayes_Workshop) MRP in RStanarm: https://github.com/lauken13/rstanarm/blob/master/vignettes/mrp.Rmd (https://github.com/lauken13/rstanarm/blob/master/vignettes/mrp.Rmd) Choosing your rstanarm prior with prior predictive checks: https://github.com/stan-dev/rstanarm/blob/vignette-prior-predictive/vignettes/prior-pred.Rmd (https://github.com/stan-dev/rstanarm/blob/vignette-prior-predictive/vignettes/prior-pred.Rmd) Mister P -- What’s its secret sauce?: https://statmodeling.stat.columbia.edu/2013/10/09/mister-p-whats-its-secret-sauce/ (https://statmodeling.stat.columbia.edu/2013/10/09/mister-p-whats-its-secret-sauce/) Bayesian Multilevel Estimation with Poststratification -- State-Level Estimates from National Polls:... Support this podcast
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/ (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 (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-zweig (https://www.stern.nyu.edu/faculty/bio/benjamin-zweig) Revelio Labs blog: https://www.reveliolabs.com/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.pdf (https://people.ischool.berkeley.edu/~hal/Papers/2013/pred-present-with-bsts.pdf) A Hierarchical Framework for CorrectingUnder-Reporting in Count Data: https://arxiv.org/pdf/1809.00544.pdf (https://arxiv.org/pdf/1809.00544.pdf) TensorFlow Probability module for Bayesian structural time series models: https://www.tensorflow.org/probability/api_docs/python/tfp/sts/ (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.html (https://www.unofficialgoogledatascience.com/2017/07/fitting-bayesian-structural-time-series.html) CausalImpact, an R package for causal inference using Bayesian structural time-series models: https://cran.r-project.org/web/packages/CausalImpact/vignettes/CausalImpact.html (https://cran.r-project.org/web/packages/CausalImpact/vignettes/CausalImpact.html) This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast
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/ (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 (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: "Matchmaking Dinner" announcement: https://twitter.com/alex_andorra/status/1351136756087734272 (https://twitter.com/alex_andorra/status/1351136756087734272) How to get acces to "Matchmaking Dinner" episodes: https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) Peadar on Twitter: https://twitter.com/springcoil (https://twitter.com/springcoil) Peadar's website: https://peadarcoyle.com/ (https://peadarcoyle.com/) Peadar on GitHub: https://github.com/springcoil (https://github.com/springcoil) Interviews with Data Scientists -- A discussion of the Industy and the current trends: https://leanpub.com/interviewswithdatascientists/ (https://leanpub.com/interviewswithdatascientists/) Aflorithmic -- Personalized Audio SaaS Platform: https://www.aflorithmic.ai/ (https://www.aflorithmic.ai/) Peadar's PyMC3 Primer: https://product.peadarcoyle.com/ (https://product.peadarcoyle.com/) This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast
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/ (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 (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Michael's website: https://faculty.sites.uci.edu/mdlee/ (https://faculty.sites.uci.edu/mdlee/) Michael on GitHub: https://twitter.com/mdlBayes (https://twitter.com/mdlBayes) Bayesian Cognitive Modeling book: https://faculty.sites.uci.edu/mdlee/bgm/ (https://faculty.sites.uci.edu/mdlee/bgm/) Bayesian Cognitive Modeling in PyMC3: https://github.com/pymc-devs/resources/tree/master/BCM (https://github.com/pymc-devs/resources/tree/master/BCM) An application of multinomial processing tree models and Bayesian methods to understanding memory impairment: https://drive.google.com/file/d/1NHml_YUsnpbUaqFhu0h8EiLeJCx6q403/view (https://drive.google.com/file/d/1NHml_YUsnpbUaqFhu0h8EiLeJCx6q403/view) Understanding the Complexity of Simple Decisions -- Modeling Multiple Behaviors and Switching Strategies: https://webfiles.uci.edu/mdlee/LeeGluckWalsh2018.pdf (https://webfiles.uci.edu/mdlee/LeeGluckWalsh2018.pdf) Robust Modeling in Cognitive Science: https://link.springer.com/article/10.1007/s42113-019-00029-y (https://link.springer.com/article/10.1007/s42113-019-00029-y) This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast
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/ (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 (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Brandon's website: https://brandonwillard.github.io/ (https://brandonwillard.github.io/) Brandon on GitHub: https://github.com/brandonwillard (https://github.com/brandonwillard) The 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-d8005f8a0e9b (https://pymc-devs.medium.com/the-future-of-pymc3-or-theano-is-dead-long-live-theano-d8005f8a0e9b) New Theano-PyMC library: https://github.com/pymc-devs/Theano-PyMC (https://github.com/pymc-devs/Theano-PyMC) Symbolic PyMC: https://pymc-devs.github.io/symbolic-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.html (https://brandonwillard.github.io/a-role-for-symbolic-computation-in-the-general-estimation-of-statistical-models.html) Symbolic Math in PyMC3: https://brandonwillard.github.io/symbolic-math-in-pymc3.html (https://brandonwillard.github.io/symbolic-math-in-pymc3.html) Dynamic Linear Models in Theano: https://brandonwillard.github.io/dynamic-linear-models-in-theano.html (https://brandonwillard.github.io/dynamic-linear-models-in-theano.html) Symbolic PyMC Radon Example in PyMC4: https://brandonwillard.github.io/symbolic-pymc-radon-example-in-pymc4.html Support this podcast
I’ll be honest here: I had a hard time summarizing this episode for you, and, let’s face it, it’s all my guest’s fault! Why? Because Aki Vehtari works on so many interesting projects that it’s hard to sum them all up, even more so because he was very generous with his time for this episode! But let’s try anyway, shall we? So, Aki is an Associate professor in computational probabilistic modeling at Aalto University, Finland. You already heard his delightful Finnish accent on episode 20, with Andrew Gelman and Jennifer Hill, talking about their latest book, « Regression and other stories ». He is also a co-author of the popular and awarded book « Bayesian Data Analysis », Third Edition, and a core-developer of the seminal probabilistic programming framework Stan. An enthusiast of open-source software, Aki is a core-contributor to the ArviZ package and has been involved in many free software projects such as GPstuff for Gaussian processes and ELFI for likelihood inference. His numerous research interests are Bayesian probability theory and methodology, especially model assessment and selection, non-parametric models (such as Gaussian processes), feature selection, dynamic models, and hierarchical models. We talked about all that — and more — on this episode, in the context of his teaching at Aalto and the software-assisted Bayesian workflow he’s currently working on with a group of researchers. 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, 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 (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: New podcast website: https://www.learnbayesstats.com/ (https://www.learnbayesstats.com/) Rate LBS on Podchaser: https://www.podchaser.com/podcasts/learning-bayesian-statistics-932588 (https://www.podchaser.com/podcasts/learning-bayesian-statistics-932588) Aki's website: https://users.aalto.fi/~ave/ (https://users.aalto.fi/~ave/) Aki's educational material: https://avehtari.github.io/ (https://avehtari.github.io/) Aki on GitHub: https://github.com/avehtari (https://github.com/avehtari) Aki on Twitter: https://twitter.com/avehtari (https://twitter.com/avehtari) Bayesian Data Analysis, 3rd edition: https://www.routledge.com/Bayesian-Data-Analysis/Gelman-Carlin-Stern-Dunson-Vehtari-Rubin/p/book/9781439840955 (https://www.routledge.com/Bayesian-Data-Analysis/Gelman-Carlin-Stern-Dunson-Vehtari-Rubin/p/book/9781439840955) Bayesian Data Analysis course material: https://github.com/avehtari/BDA_course_Aalto (https://github.com/avehtari/BDA_course_Aalto) Regression and Other Stories: https://avehtari.github.io/ROS-Examples/ (https://avehtari.github.io/ROS-Examples/) Aki’s favorite scientific books (so far): https://statmodeling.stat.columbia.edu/2018/05/14/aki_books/ (https://statmodeling.stat.columbia.edu/2018/05/14/aki_books/) Aki's talk on Agile Probabilistic Programming: https://www.youtube.com/watch?v=cHlPgHn6btg (https://www.youtube.com/watch?v=cHlPgHn6btg) Aki's posts on Andrew Gelman's blog: https://statmodeling.stat.columbia.edu/author/aki/ (https://statmodeling.stat.columbia.edu/author/aki/) Stan software: https://mc-stan.org/ (https://mc-stan.org/) GPstuff - Gaussian... Support this podcast
In times of crisis, designing an efficient policy response is paramount. In case of natural disasters or pandemics, it can even determine the difference between life and death for a substantial number of people. But precisely, how do you design such policy responses, making sure that risks are optimally shared, people feel safe enough to reveal necessary information, and stakeholders commit to the policies? That’s where a field of economics, industrial organization (IO), can help, as Shosh Vasserman will tell us in this episode. Shosh is an assistant professor of economics at the Stanford Graduate School of Business. Specialized in industrial organization, her interests span a number of policy settings, such as public procurement, pharmaceutical pricing and auto-insurance. Her work leverages theory, empirics and modern computation (including the Stan software!) to better understand the equilibrium implications of policies and proposals involving information revelation, risk sharing and commitment.  In short, Shoshana uses theory and data to study how risk, commitment and information flows interplay with policy design. And she does a lot of this with… Bayesian models! Who said Bayes had no place in economics? Prior to Stanford, Shoshana did her Bachelor’s in mathematics and economics at MIT, and then her PhD in economics at Harvard University. This was a fascinating conversation where I learned a lot about Bayesian inference on large scale random utility logit models, socioeconomic network heterogeneity and pandemic policy response — and I’m sure you will too! 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, 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 and Paul Oreto. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Shosh's website: https://shoshanavasserman.com/ (https://shoshanavasserman.com/) Shosh on Twitter: https://twitter.com/shoshievass (https://twitter.com/shoshievass) How do different reopening strategies balance health and employment: https://reopenmappingproject.com/ (https://reopenmappingproject.com/) Aggregate random coefficients logit—a generative approach: http://modernstatisticalworkflow.blogspot.com/2017/03/aggregate-random-coefficients-logita.html (http://modernstatisticalworkflow.blogspot.com/2017/03/aggregate-random-coefficients-logita.html) Voluntary Disclosure and Personalized Pricing: https://shoshanavasserman.com/files/2020/08/Voluntary-Disclosure-and-Personalized-Pricing.pdf (https://shoshanavasserman.com/files/2020/08/Voluntary-Disclosure-and-Personalized-Pricing.pdf) Socioeconomic Network Heterogeneity and Pandemic Policy Response: https://shoshanavasserman.com/files/2020/06/Network-Heterogeneity-Pandemic-Policy.pdf (https://shoshanavasserman.com/files/2020/06/Network-Heterogeneity-Pandemic-Policy.pdf) Buying Data from Consumers -- The Impact of Monitoring Programs in U.S. Auto Insurance: https://shoshanavasserman.com/files/2020/05/jinvass_0420.pdf (https://shoshanavasserman.com/files/2020/05/jinvass_0420.pdf) This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast
In a few days, a consequential election will take place, as citizens of the United States will go to the polls and elect their president — in fact they already started voting. You probably know a few forecasting models that try to predict what will happen on Election Day — who will get elected, by how much and with which coalition of States? But how do these statistical models work? How do you account for the different sources of uncertainty, be it polling errors, unexpected turnout or media events? How do you model covariation between States? How do you even communicate the model’s results and afterwards assess its performance? To talk about all this, I had the pleasure to talk to Andrew Gelman and Merlin Heidemanns. Andrew was already on episode 20, to talk about his recent book with Jennifer Hill and Aki Vehtari, “Regression and Other Stories”. He’s a professor of statistics and political science at Columbia University and works on a lot of topics, including: why campaign polls are so variable while elections are so predictable, the statistical challenges of estimating small effects, and methods for surveys and experimental design. Merlin is a PhD student in Political Science at Columbia University, and he specializes in political methodology. Prior to his PhD, he did a Bachelor's in Political Science at the Freie Universität Berlin. I hope you’ll enjoy this episode where we dove into the Bayesian model they helped develop for The Economist, and talked more generally about how to forecast elections with statistical methods, and even about the incentives the forecasting industry has as a whole. Thank you to my Patrons for making this episode possible! Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) 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/) ! Links from the show: Andrew's website: http://www.stat.columbia.edu/~gelman/ (http://www.stat.columbia.edu/~gelman/) Andrew's blog: https://statmodeling.stat.columbia.edu/ (https://statmodeling.stat.columbia.edu/) Andrew on Twitter: https://twitter.com/statmodeling (https://twitter.com/statmodeling) Merlin's website: https://merlinheidemanns.github.io/website/ (https://merlinheidemanns.github.io/website/) Merlin on Twitter: https://twitter.com/MHeidemanns (https://twitter.com/MHeidemanns) The Economist POTUS forecast: https://projects.economist.com/us-2020-forecast/president (https://projects.economist.com/us-2020-forecast/president) How The Economist presidential forecast works: https://projects.economist.com/us-2020-forecast/president/how-this-works (https://projects.economist.com/us-2020-forecast/president/how-this-works) GitHub repo of the Economist model: https://github.com/TheEconomist/us-potus-model (https://github.com/TheEconomist/us-potus-model) Information, incentives, and goals in election forecasts (Gelman, Hullman & Wlezien): http://www.stat.columbia.edu/~gelman/research/unpublished/forecast_incentives3.pdf (http://www.stat.columbia.edu/~gelman/research/unpublished/forecast_incentives3.pdf) How to think about extremely unlikely events: https://bit.ly/3ejZYyZ (https://bit.ly/3ejZYyZ) Postal voting could put America’s Democrats at a disadvantage: https://econ.st/3mCxR0P (https://econ.st/3mCxR0P) 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... Support this podcast
I don’t know about you, but I’m starting to really miss traveling and just talking to people without having to think about masks, social distance and activating the covid tracking app on my phone. In the coming days, there is one event that, granted, won’t make all of that disappear, but will remind me how enriching it is to meet new people — this event is PyMCon, the first-ever conference about the PyMC ecosystem! To talk about the conference format, goals and program, I had the pleasure to host Ravin Kumar and Quan Nguyen on the show. Quan is a PhD student in computer science at Washington University in St Louis, USA, researching Bayesian machine learning and one of the PyMCon program committee chairs. He is also the author of several programming books on Python and scientific computing. Ravin is a core contributor to Arviz and PyMC, and is leading the PyMCon conference. He holds a Bachelors in Mechanical Engineering and a Masters in Manufacturing Engineering. As a Principal Data Scientist he has used Bayesian Statistics to characterize and aid decision making at organizations like SpaceX and Sweetgreen. Ravin is also currently co-authoring a book with Ari Hartikainen, Osvaldo Martin, and Junpeng Lao on Bayesian Statistics due for release in February. We talked about why they became involved in the conference, parsed through the numerous, amazing talks that are planned, and detailed who the keynote speakers will be… So, If you’re interested the link to register is in the show notes, and there are even two ways to get a free ticket: either by applying to a diversity scholarship, or by being a community partner, which is anyone or any organization working towards diversity and inclusion in tech — all links are in the show notes. 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/) ! Links from the show: PyMCon speakers: https://pymc-devs.github.io/pymcon/speakers (https://pymc-devs.github.io/pymcon/speakers) Register to PyMCon: https://www.eventbrite.com/e/pymcon-2020-tickets-121404065829 (https://www.eventbrite.com/e/pymcon-2020-tickets-121404065829) PyMCon Diversity Scholarship: https://bit.ly/2J3Vb9d (https://bit.ly/2J3Vb9d) PyMCon Community Partner Form: https://bit.ly/35yq90L (https://bit.ly/35yq90L) PyMC3 -- Probabilistic Programming in Python: https://docs.pymc.io (https://docs.pymc.io) Donate to PyMC3: https://numfocus.org/donate-to-pymc3 (https://numfocus.org/donate-to-pymc3) PyMC3 for enterprise: https://bit.ly/3jo9jq9 (https://bit.ly/3jo9jq9) Ravin on Twitter: https://twitter.com/canyon289 (https://twitter.com/canyon289) Quan on the web: https://krisnguyen135.github.io/ (https://krisnguyen135.github.io/) Quan's author page: https://amzn.to/37JsB7r (https://amzn.to/37JsB7r) Alex talks about polls on the "Local Maximum" podcast: https://bit.ly/3e1Ro7O (https://bit.ly/3e1Ro7O) Support "Learning Bayesian Statistics" on Patreon: https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) 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 and Paul Oreto. This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast
Have you watched the series « The English Game », on Netflix? Well, I think you should — it’s a fascinating dive into how football went from an aristocratic to a popular sport in the late 19th century England. Today it is so popular that it became a valuable business to do statistics on the game and its players! To talk about that, I invited Kevin Minkus on the show — he’s a data scientist and soccer fan living in Philadelphia. Kevin’s currently working at Monetate on ecommerce problems, and prior to Monetate he worked on property and casualty insurance pricing. He spends a lot of his spare time working on problems in football analytics and is a contributor at American Soccer Analysis, a website and podcast dedicated to… football made or played in the US (or “soccer”, as they say over there). Kevin is responsible for some of their data management and devops, and he recently wrote a guide to football analytics for the Major League Soccer’s website, entitled « Soccer Analytics 101 ». To be honest, I had a great time talking for one hour about two of my passions — football and stats! Soooo, maybe 2020 isn’t that bad after all… Ow, and beyond football, Kevin is also into the digital humanities, web development, 3D animation, machine learning, and… the bassoon! 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/) ! Links from the show: Kevin on Twitter: https://twitter.com/kevinminkus (https://twitter.com/kevinminkus) Kevin on GitHub: https://github.com/kcm30 (https://github.com/kcm30) Soccer Analytics 101: https://www.mlssoccer.com/soccer-analytics-guide/2020/soccer-analytics-101 (https://www.mlssoccer.com/soccer-analytics-guide/2020/soccer-analytics-101) American Soccer Analysis: https://www.americansocceranalysis.com/ (https://www.americansocceranalysis.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 and Paul Oreto. This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast
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Reynold Okudzeto

hey, this is awesome!

Feb 8th
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