Learning Bayesian Statistics

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!A great franchise comes with a great rivalry: Marvel has Iron Man and Captain America; physics has General Relativity and Quantum Physics; and Bayesian stats has Posterior Estimation and… Bayes Factors!A few months ago, I had the pleasure of hosting EJ Wagenmakers, to talk about these topics. This time, I’m talking with Jorge Tendeiro, who has a different perspective on Null Hypothesis Testing in the Bayesian framework, and its relationship with generative models and posterior estimation.But this is not your classic, click-baity podcast, and I’m not interested in pitching people against each other. Instead, you’ll hear Jorge talk about the other perspective fairly, before even giving his take on the topic. Jorge will also tell us about the difficulty of arguing through papers, and all the nuances you lose compared to casual discussions.But who is Jorge Tendeiro? He is a professor at Hiroshima University in Japan, and he was recommended to me by Pablo Bernabeu, a listener of this very podcast.Before moving to Japan, Jorge studied math and applied stats at the University of Porto, and did his PhD in the Netherlands. He focuses on item response theory (specifically person fit analysis), and, of course, Bayesian statistics, mostly Bayes factors.He’s also passionate about privacy issues in the 21st century, an avid Linux user since 2006, and is trying to get the hang of the Japanese language.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, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas and Robert Yolken.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Jorge’s website: https://www.jorgetendeiro.com/Jorge on Twitter: https://twitter.com/jntendeiroJorge on GitHub: https://github.com/jorgetendeiroA Review of Issues About Null Hypothesis Bayesian Testing:  https://pure.rug.nl/ws/portalfiles/portal/159021509/2019_26880_001.pdfAdvantages Masquerading as ‘Issues’ in Bayesian Hypothesis Testing – A Commentary on Tendeiro and Kiers: https://psyarxiv.com/nf7rpOn the white, the black, and the many shades of gray in between – Our reply to van Ravenzwaaij and Wagenmakers: https://psyarxiv.com/tjxvz/LBS #61, Why we still use non-Bayesian methods, with EJ Wagenmakers: https://learnbayesstats.com/episode/61-why-we-still-use-non-bayesian-methods-ej-wagenmakers/LBS #67, Exoplanets, Cool Worlds & Life in the Universe, with David Kipping: https://learnbayesstats.com/episode/67-exoplanets-cool-worlds-life-in-universe-david-kipping/

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Hosting someone like Kevin Murphy on your podcast is… complicated. Not because Kevin himself is complicated (he’s delightful, don’t make me say what I didn’t say!), but because all the questions I had for him amounted to a 12-hour show.Sooooo, brace yourselves folks!No, I'm kidding. Of course I didn’t do that folks, Kevin has a life! This life started in Ireland, where he was born. He grew up in England and got his BA from the University of Cambridge. After his PhD at UC Berkeley, he did a postdoc at MIT, and was an associate professor of computer science and statistics at the University of British Columbia in Vancouver, Canada, from 2004 to 2012. After getting tenure, he went to Google in California in 2011 on his sabbatical and then ended up staying. He currently runs a team of about 8 researchers inside of Google Brain working on generative models, optimization, and other, as Kevin puts it, “basic” research topics in AI/ML. He has published over 125 papers in refereed conferences and journals, as well 3 textbooks on machine learning published in 2012, 2022 and the last one coming in 2023. You may be familiar with his 2012 book, as it was awarded the DeGroot Prize for best book in the field of statistical science.Outside of work, Kevin enjoys traveling, outdoor sports (especially tennis, snowboarding and scuba diving), as well as reading, cooking, and spending time with his family.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, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha, Scott Anthony Robson, David Haas and Robert Yolken.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Kevin’s website: https://www.cs.ubc.ca/~murphyk/Kevin on Twitter: https://mobile.twitter.com/sirbayesKevin’s books (free pdf) on GitHub (includes a link to places where you can buy the hard copy): https://probml.github.io/pml-book/Book that inspired Kevin to get into AI: https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567State-space models library in JAX (WIP): https://github.com/probml/ssm-jaxOther software for the book (also in JAX): https://github.com/probml/pyprobmlFun photo of Kevin’s hacked-up wearable camera system from 20 years ago: https://www.cs.ubc.ca/~murphyk/Vision/placeRecognition.html

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Is there life in the Universe? It doesn’t get deeper than this, does it? And yet, why do we care about that? In the very small chance that there is other life in the Universe, we have even less chance to discover it, talk to it and meet it. So, why do we care?Well, it may surprise you but Bayesian statistics helps us think about these astronomical and — dare I say? — philosophical topics, as my guest, David Kipping, will brilliantly explain in this episode.David is an Associate Professor of Astronomy at Columbia University, where he leads the Cool Worlds Lab — I know, the name is awesome. His team’s research spans exoplanet discovery and characterization, the search for life in the Universe and developing novel approaches to our exploration of the cosmos.David also teaches astrostatistics, and his contributions to Bayesian statistics span astrobiology to exoplanet detection. He also hosts the Cool Worlds YouTube channel, with over half a million subscribers, that discusses his team’s work and broader topics within the field.Cool worlds, cool guest, cool episode.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, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha, Scott Anthony Robson, David Haas and Robert Yolken.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:David’s website: http://user.astro.columbia.edu/~dkipping/David on Twitter: https://twitter.com/david_kippingDavid’s YouTube channel: https://www.youtube.com/c/coolworldslabDavid’s research group: https://www.coolworldslab.com/Bayesian analysis of the astrobiological implications of life’s early emergence on Earth : https://www.pnas.org/doi/10.1073/pnas.1111694108We Have No Idea – A Guide to the Unknown Universe : https://www.goodreads.com/book/show/31625636-we-have-no-ideaLeonardo da Vinci’s biography by Walter Isaacson: https://www.amazon.com/Leonardo-Vinci-Walter-Isaacson/dp/1501139169/ref=sr_1_1?keywords=leonardo+da+vinci+book&qid=1660142880&sprefix=leonardo+%2Caps%2C219&sr=8-1

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!I have to confess something: I love challenges. And when you’re a podcaster, what’s a better challenge than dedicating an episode to… visualization? Impossible you say? Well, challenge accepted!Thankfully, I got the help of a visualization Avenger for this episode — namely, Matthew Kay. Matt is an Assistant Professor jointly appointed in Computer Science and Communications Studies at Northwestern University, where he co-directs the Midwest Uncertainty Collective — I know, it’s a pretty cool name for a lab.He works in human-computer interaction and information visualization, and especially in uncertainty visualization. He also builds tools to support uncertainty visualization in R. In particular, he’s the author of the tidybayes and ggdist R packages, and wrote the random variable interface in the posterior package.I promise, you won’t be uncertain about the importance of uncertainty visualization after that…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, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha and Scott Anthony Robson.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Matt on Twitter: https://twitter.com/mjskayMatt on GitHub: https://github.com/mjskay  Matt’s website: https://www.mjskay.com/ Midwest Uncertainty Collective lab: https://mucollective.northwestern.edu/ PyMC find_constrained_priors tutorial: https://www.youtube.com/watch?v=9shZeqKG3M0PyMC find_constrained_priors doc: https://www.pymc.io/projects/docs/en/latest/api/generated/pymc.find_constrained_prior.htmlTutorials / package documentation / videos:tidybayes: http://mjskay.github.io/tidybayes/ ggdist: https://mjskay.github.io/ggdist/ (various visualizations in the slabinterval vignette: https://mjskay.github.io/ggdist/articles/slabinterval.html ) Miscellaneous uncertainty visualizations examples: https://github.com/mjskay/uncertainty-examples Talk on uncertainty visualization: https://www.youtube.com/watch?v=E1kSnWvqCw0 Biases in probability perception:A survey paper on the linear-in-log-odds model of probability perception: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261445/Using the linear-in-log-odds model to "debias" uncertainty visualization: https://osf.io/6xcnw/ 

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Folks, there are some new cool kids on the block. They are called PyMC, Aeppl, and Aesara, and it’s high time we give us a proper welcome!To do that, who better than one of the architects of the new PyMC 4.0 — Ricardo Vieira! In this episode, he’ll walk us through the inner workings of the newly released version of PyMC, telling us why the Aesara backend and the brand new RandomVariable operators constitute such strong foundations for your beloved PyMC models. He will also tell us about a self-contained PPL project called Aeppl, dedicated to converting model graphs to probability functions — pretty cool, right?Oh, in case you didn’t guess yet, Ricardo is a PyMC developer and data scientist at PyMC Labs. He spent several years teaching himself Statistics and Computer Science at the expense of his official degrees in Psychology and Neuroscience.So, get ready for efficient random generator functions, better probability evaluation functions, and a fully-fledged modern Bayesian workflow!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, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha and Scott Anthony Robson.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Ricardo on Twitter: https://twitter.com/RicardoV944Ricardo on GitHub: https://github.com/ricardoV94/Ricardo’s website: https://ricardov94.github.io/posts/PyMC, Aesara and Aeppl: The New Kids on The Block (YouTube video): https://www.youtube.com/watch?v=_APNiXTfYJwBayesian Vector Autoregression in PyMC: https://www.pymc-labs.io/blog-posts/bayesian-vector-autoregression/New PyMC website: https://www.pymc.io/projects/docs/en/stable/learn.htmlDefine, optimize, and evaluate mathematical expressions with Aesara: https://aesara.readthedocs.io/en/latest/Aeppl documentation: https://aeppl.readthedocs.io/en/latest/PyMC’s YouTube channel: https://www.youtube.com/c/PyMCDevelopersPyMC on Twitter: https://twitter.com/pymc_devsPyMC on LinkedIn: https://www.linkedin.com/company/pymc/mycompany/LBS #61, Why we still use non-Bayesian methods, with EJ Wagenmakers: https://www.learnbayesstats.com/episode/61-why-we-still-use-non-bayesian-methods-ej-wagenmakersLBS #31, Bayesian Cognitive Modeling & Decision-Making, with Michael Lee:  https://www.learnbayesstats.com/episode/31-bayesian-cognitive-modeling-michael-leeWe Have No Idea, A Guide to the Unknown Universe: https://www.amazon.com/We-Have-No-Idea-Universe/dp/0735211515

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!I’m sure you’ve already heard of gravitational waves, because my listeners are the coolest and smartest ever ;) But did you know about gravity waves? That’s right, waves in the sky due to gravity — sounds awesome, right?Well, I’m pretty sure that Laura Mansfield will confirm your prior. Currently a postdoc at Stanford University, Laura studies — guess what? — gravity waves and how they are represented in climate models. In particular, she uses Bayesian methods to estimate the uncertainty on the gravity wave components of the models.Holding a PhD from the University of Reading in the UK, her background is in atmospheric physics, but she’s interested in climate change and environmental issues.So seat back, chill out, and enjoy this physics-packed, aerial episode!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, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha and Scott Anthony Robson.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Laura on Twitter: https://twitter.com/lau_mansfieldLaura’s webpage: https://profiles.stanford.edu/laura-mansfieldJulia package for Gaussian Processes: https://github.com/STOR-i/GaussianProcesses.jl Julia implementation of the scikit-learn API: https://github.com/cstjean/ScikitLearn.jlDerivative-free Bayesian optimization techniques based on Ensemble Kalman Filters: https://github.com/CliMA/EnsembleKalmanProcesses.jl

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Inviting someone like Luciano Paz on a stats podcast is both a pleasure and a challenge — he does so many things brilliantly that you have too many questions to ask him…In this episode, I’ve chosen — not without difficulty — to focus on the applications of Bayesian stats in the marketing industry, especially Media Mix Models. Ok, I also asked Luciano about other topics — but you know me, I like to talk…Originally, Luciano studied physics. He then did a PhD and postdoc in neuroscience, before transitioning into industry. During his time in academia, he used stats, machine learning and data science concepts here and there, but not in a very organized way.But at the end of his postdoc, he got into PyMC — and that’s when everything changed… He loved the community and decided to hop on board to exit academia into a better life. After leaving academia, he worked at a company that wanted to do data science but that, for privacy reasons, didn’t have a lot of data. And now, Luciano is one of the folks working full time at the PyMC Labs consultancy.But Luciano is not only one of the cool nerds building this crazy Bayesian adventures. He also did a lot of piano and ninjutsu. Sooooo, don’t provoke him — either in the streets or at a karaoke bar…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, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh and Lin Yu Sha.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Luciano’s website: https://lucianopaz.github.io/Luciano on GitHub: https://github.com/lucianopazLuciano on LinkedIn: https://www.linkedin.com/in/luciano-paz-4139b5123/Bayesian Media Mix Modeling for Marketing Optimization: https://www.pymc-labs.io/blog-posts/bayesian-media-mix-modeling-for-marketing-optimization/Improving the Speed and Accuracy of Bayesian Media Mix Models: https://www.pymc-labs.io/blog-posts/reducing-customer-acquisition-costs-how-we-helped-optimizing-hellofreshs-marketing-budget/Speeding up HelloFresh's Bayesian AB tests by 60x: https://www.pymc-labs.io/blog-posts/bayes-is-slow-speeding-up-hellofreshs-bayesian-ab-tests-by-60x/PyMC Labs YouTube channel: https://www.youtube.com/c/PyMCLabsLBS #21 Gaussian Processes, Bayesian Neural Nets & SIR Models, with Elizaveta Semenova: https://www.learnbayesstats.com/episode/21-gaussian-processes-bayesian-neural-nets-sir-models-with-elizaveta-semenovaGaussian Processes approximations in PyMC: https://github.com/pymc-devs/pymc-experimental/pull/3Michael Betancourt, ​​Identifying Bayesian Mixture Models: https://betanalpha.github.io/assets/case_studies/identifying_mixture_models.htmlIdentifying Bayesian Mixture Models in PyMC3: https://gist.github.com/junpenglao/4d65d1a9bf80e8d371446fadda9deb7aMixture Models in PyMC: https://www.pymc.io/projects/examples/en/latest/gallery.html#mixture-modelsOsvaldo Martin’s Bayesian Analysis with Python: https://www.amazon.com/dp/B07HHBCR9GLBS #4 Dirichlet Processes and Neurodegenerative Diseases, with Karin Knudson: https://www.learnbayesstats.com/episode/4-dirichlet-processes-and-neurodegenerative-diseases-with-karin-knudsonIntuitive Bayes Introductory Course: https://www.intuitivebayes.com/

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!We talk a lot about generative modeling on this podcast — at least since episode 6, with Michael Betancourt! And an area where this way of modeling is particularly useful is healthcare, as Maria Skoularidou will tell us in this episode.Maria is a final year PhD student at the University of Cambridge. Her thesis is focused on probabilistic machine learning and, more precisely, towards using generative modeling in… you guessed it: healthcare!But her fields of interest are diverse: from theory and methodology of machine intelligence to Bayesian inference; from theoretical computer science to information theory — Maria is knowledgeable in a lot of topics! That’s why I also had to ask her about mixture models, a category of models that she uses frequently.Prior to her PhD, Maria studied Computer Science and Statistical Science at Athens University of Economics and Business. She’s also invested in several efforts to bring more diversity and accessibility in the data science world.When she’s not working on all this, you’ll find her playing the ney, trekking or rawing.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton and Jeannine Sue.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Maria on Twitter: https://twitter.com/skoularidouMaria on LinkedIn: https://www.linkedin.com/in/maria-skoularidou-1289b62a/Maria’s webpage:https://www.mrc-bsu.cam.ac.uk/people/in-alphabetical-order/n-to-s/maria-skoularidou/Mixture models in PyMC: https://www.pymc.io/projects/examples/en/latest/gallery.html#mixture-modelsLBS #4 Dirichlet Processes and Neurodegenerative Diseases, with Karin Knudson: https://learnbayesstats.com/episode/4-dirichlet-processes-and-neurodegenerative-diseases-with-karin-knudson/Bayesian mixtures with an unknown number of components: https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00095Markov Chain sampling methods for Dirichlet Processes: https://www.tandfonline.com/doi/abs/10.1080/10618600.2000.10474879Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models: https://academic.oup.com/biomet/article-abstract/95/1/169/219181Sampling Dirichlet mixture models with slices: https://www.tandfonline.com/doi/abs/10.1080/03610910601096262Label switching problem:https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00265Mixture Models With a Prior on the Number of Components: https://www.tandfonline.com/doi/abs/10.1080/01621459.2016.1255636Approximate Bayesian inference for Gaussian models (R-INLA): https://www.r-inla.orgIntuitive Bayes Introductory Course: https://www.intuitivebayes.com/PyMC Labs corporate workshops: https://www.pymc-labs.io/workshopsLBS #44 Building Bayesian Models at scale, with Rémi Louf: https://www.learnbayesstats.com/episode/44-bayesian-models-at-scale-remi-loufBlackjax – Sampling library designed for ease of use, speed and modularity: https://blackjax-devs.github.io/blackjax/

The big problems with classic hypothesis testing are well-known. And yet, a huge majority of statistical analyses are still conducted this way. Why is it? Why are things so hard to change? Can you even do (and should you do) hypothesis testing in the Bayesian framework?I guess if you wanted to name this episode in a very Marvelian way, it would be “Bayes factors against the p-values of madness” — but we won’t do that, it wouldn’t be appropriate, would it?Anyways, in this episode, I’ll talk about all these very light and consensual topics with Eric-Jan Wagenmakers, a professor at the Psychological Methods Unit of the University of Amsterdam.For almost two decades, EJ has staunchly advocated the use of Bayesian inference in psychology. In order to lower the bar for the adoption of Bayesian methods, he is coordinating the development of JASP, an open-source software program that allows practitioners to conduct state-of-the-art Bayesian analyses with their mouse — the one from the computer, not the one from Disney.EJ has also written a children’s book on Bayesian inference with the title “Bayesian thinking for toddlers”. Rumor has it that he is also working on a multi-volume series for adults — but shhh, that’s a secret!EJ’s lab publishes regularly on a host of Bayesian topics, so check out his website, particularly when you are interested in Bayesian hypothesis testing. The same goes for his blog by the way, “BayesianSpectacles”.Wait, what’s that? EJ is telling me that he plays chess, squash, and that, most importantly, he enjoys watching arm wrestling videos on YouTube — yet another proof that, yes, you can find everything on YouTube.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, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland and Aubrey Clayton.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:EJ’s website: http://ejwagenmakers.com/EJ on Twitter: https://twitter.com/EJWagenmakers“Bayesian Cognitive Modeling” book website: https://bayesmodels.com/Port of “Bayesian Cognitive Modeling” to PyMC: https://github.com/pymc-devs/pymc-resources/tree/main/BCMEJ’s blog: http://www.bayesianspectacles.org/JASP software website: https://jasp-stats.org/Bayesian Thinking for Toddlers: https://psyarxiv.com/w5vbp/LBS #31, Bayesian Cognitive Modeling & Decision-Making with Michael Lee: https://www.learnbayesstats.com/episode/31-bayesian-cognitive-modeling-michael-lee“You can't play 20 questions with nature and win”: https://www.coli.uni-saarland.de/~crocker/documents/Newell-1973.pdfApplying Occam's razor in modeling cognition – A Bayesian approach: https://link.springer.com/article/10.3758/BF03210778Adjusting for publication bias in JASP & R – Selection models, PET-PEESE, and robust Bayesian meta-analysis: https://psyarxiv.com/75bqn/Robust Bayesian meta-analysis – Addressing publication bias with model-averaging: https://psyarxiv.com/u4cnsA primer on Bayesian model-averaged meta-analysis: https://journals.sagepub.com/doi/full/10.1177/25152459211031256

Why do we, humans, communicate? And how? And isn’t that a problem that to study communication we have to… communicate?Did you ever ask yourself that? Because J.P. de Ruiter did — and does everyday. But he’s got good reasons: JP is a cognitive scientist whose primary research focus is on the cognitive foundations of human communication. He aims to improve our understanding of how humans and artificial agents use language, gesture and other types of signals to effectively communicate with each other.Currently he has one of the two Bridge Professorship at Tufts University, and has been appointed in both the Computer Science and Psychology departments.In this episode, we’ll look at why Bayes is helpful in dialogue research, what the future of the field looks like to JP, and how he uses PyMC in his own teaching.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, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland and Aubrey Clayton.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:JP’s page: https://sites.tufts.edu/hilab/people/Projecting the End of a Speaker's Turn – A Cognitive Cornerstone of Conversation: https://www.researchgate.net/publication/236787756_Projecting_the_End_of_a_Speaker's_Turn_A_Cognitive_Cornerstone_of_ConversationCognitive and social delays in the initiation of conversational repair: https://journals.uic.edu/ojs/index.php/dad/article/view/11388Using uh and um in spontaneous speaking: http://www.columbia.edu/~rmk7/HC/HC_Readings/Clark_Fox.pdfStatus of Frustrator as an Inhibitor of Horn-Honking Responses: https://www.tandfonline.com/doi/abs/10.1080/00224545.1968.9933615A Simplest Systematics for the Organization of Turn-Taking for Conversation: https://www.jstor.org/stable/412243Richard McElreath, Science Before Statistics – Intro to Causal Inference: https://www.youtube.com/watch?v=KNPYUVmY3NMThe Prosecutor's fallacy: https://en.wikipedia.org/wiki/Prosecutor%27s_fallacyThe Monty Hall problem: https://en.wikipedia.org/wiki/Monty_Hall_problemLBS #50, Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://www.learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalterLBS #51, Bernoulli’s Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://www.learnbayesstats.com/episode/51-bernoullis-fallacy-crisis-modern-science-aubrey-claytonPyMC port of Lee and Wagenmakers' Bayesian Cognitive Modeling: https://github.com/pymc-devs/pymc-resources/tree/main/BCMArviZ documentation: https://arviz-devs.github.io/arviz/