Learning Bayesian Statistics

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/

In large-scale one-off civil infrastructure, decision-making under uncertainty is part of the job, that’s just how it is. But, civil engineers don't get the luxury of building 10^6 versions of the bridge, offshore wind turbine or aeronautical structure to consider a relative frequency interpretation!And as you’ll hear, challenges don’t stop there: you also have to consider natural hazards such as earthquakes, rockfall and typhoons — in case you were wondering, civil engineering is not among the boring jobs!To talk about these original topics, I had the pleasure to host Michael Faber. Michael is a Professor at the Department of Built Environment at Aalborg University, Denmark, the President of the Joint Committee on Structural Safety and is a tremendously deep thinker on the Bayesian interpretation of probability as it pertains to the risk-informed management of big infrastructure.His research interests are directed on governance and management of risks, resilience and sustainability in the built environment — doing all that with Bayesian probabilistic modeling and applied Bayesian decision analysis, as you’ll hear.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:Michael's profile on Aalborg University: https://vbn.aau.dk/en/persons/100493Michael's LinkedIn profile: https://www.linkedin.com/in/michael-havbro-faber-22898414/Statistics and Probability Theory - In Pursuit of Engineering Decision Support: https://link.springer.com/book/10.1007/978-94-007-4056-3Bayes in Civil Engineering - an abridged personal account of research and applications: https://www.linkedin.com/pulse/bayes-civil-engineering-michael-havbro-faberWebsite of the Joint Committee on Structural Safety (JCSS): https://www.jcss-lc.org/

You know when you have friends who wrote a book and pressure you to come on your podcast? That’s super annoying, right?Well that’s not what happened with Ravin Kumar, Osvaldo Martin and Junpeng Lao — I was the one who suggested doing a special episode about their new book, Bayesian Modeling and Computation in Python. And since they cannot say no to my soothing French accent, well, they didn’t say no…All of them were on the podcast already, so I’ll refer you to their solo episode for background on their background — aka backgroundception.Junpeng is a Data Scientist at Google, living in Zurich, Switzerland. Previously, he was a post-doc in Psychology and Cognitive Neuroscience. His current obsessions are time series and state space models. Osvaldo is a Researcher at CONICET in Argentina and the Department of Computer Science from Aalto University in Finland. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling.Ravin is a data scientist at Google, living in Los Angeles. Previously he worked at Sweetgreen and SpaceX. He became interested in Bayesian statistics when trying to quantify uncertainty in operations. He is especially interested in decision science in business settings.You’ll make your own opinion, but I like their book because uses a hands-on approach, focusing on the practice of applied statistics. And you get to see how to use diverse libraries, like PyMC, Tensorflow Probability, ArviZ, Bambi, and so on. You’ll see what I’m talking about in this episode.To top it off, the book is fully available online at bayesiancomputationbook.com. If you want a physical copy (because you love those guys and wanna support them), go to CRC website and enter the code ASA18 at checkout for a 30% discount.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:Website of the book: https://bayesiancomputationbook.com/welcome.htmlLBS #1 -- Bayes, open-source and bioinformatics, with Osvaldo Martin: https://www.learnbayesstats.com/episode/1-bayes-open-source-and-bioinformatics-with-osvaldo-martinOsvaldo on Twitter: https://twitter.com/aloctavodiaLBS #26 -- What you'll learn & who you'll meet at the PyMC Conference, with Ravin Kumar & Quan Nguyen: https://www.learnbayesstats.com/episode/26-what-youll-learn-who-youll-meet-at-the-pymc-conference-with-ravin-kumar-quan-nguyenRavin's blog: https://ravinkumar.com/Ravin on Twitter: https://twitter.com/canyon289LBS #7 -- Designing a Probabilistic Programming Language & Debugging a Model, with Junpeng Lao: https://www.learnbayesstats.com/episode/7-designing-a-probabilistic-programming-language-debugging-a-model-with-junpeng-laoJunpeng on Twitter: https://twitter.com/junpenglaoMatchmaking Dinner #1, with Will Kurt and Junpeng Lao: https://www.patreon.com/posts/48360540Donate to PyMC: https://numfocus.org/pymc-bayesian-book-formDonate to ArviZ: https://numfocus.org/arviz-bayesian-book-form

No, no, don't leave! You did not click on the wrong button. You are indeed on Alex Andorra’s podcast. The podcast that took the Bayesian world by a storm: “Learning Bayesian Statistics”, and that Barack Obama deemed “the best podcast in the whole galaxy” – or maybe Alex said that, I don’t remember.Alex made us discover new methods, new ideas, and mostly new people. But what do we really know about him? Does he even really exist? To find this out I put on my Frenchest beret, a baguette under my arm, and went undercover to try to find him.And I did ! So today for a special episode I, Rémi Louf, will be the one asking questions and making bad jokes with a French accent.Before letting him in, here’s what I got on him so far.By day, Alex is a Bayesian modeler at the PyMC Labs consultancy. By night, he doesn’t (yet) fight crime but he’s an open-source enthusiast and core contributor to PyMC and ArviZ.An always-learning statistician, Alex loves building models and studying elections and human behavior.When he’s not working, he loves hiking, exercising, meditating and reading nerdy books and novels. He also loves chocolate a bit too much, but he doesn’t like talking about it – he prefers eating it.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:Alex on Twitter: https://twitter.com/alex_andorraAlex on GitHub: https://github.com/AlexAndorraAlex on LinkedIn: https://www.linkedin.com/in/aandorra-pollsposition/Intuitive Bayes Introductory Course: https://www.intuitivebayes.com/PyMC Labs consultancy: https://www.pymc-labs.io/PollsPosition GitHub repository: https://github.com/pollspositionFrench Presidents' popularity dashboard: https://www.pollsposition.com/popularityLearning Bayesian Statistics YouTube channel: https://www.youtube.com/channel/UCAwVseuhVrpJFfik_cMHrhQLove the podcast? Leave a review on Podchaser: https://www.podchaser.com/podcasts/learning-bayesian-statistics-932588

Did you know there is a relationship between the size of firetrucks and the amount of damage down to a flat during a fire? The bigger the truck sent to put out the fire, the bigger the damages tend to be. The solution is simple: just send smaller firetrucks!Wait, that doesn’t sound right, does it? Our brain is a huge causal machine, so it can instinctively feel it’s not credible that size of truck and amount of damage done are causally related: there must be another variable explaining the correlation. Here, it’s of course the seriousness of the fire — even better, it’s the common cause of the two correlated variables.Your brain does that automatically, but what about your computer? How do you make sure it doesn’t just happily (and mistakenly) report the correlation? That’s when causal inference and machine learning enter the stage, as Robert Osazuwa Ness will tell us.Robert has a PhD in statistics from Purdue University. He currently works as a Research Scientist at Microsoft Research and a founder of altdeep.ai, which teaches live cohort-based courses on advanced topics in applied modeling. As you’ll hear, his research focuses on the intersection of causal and probabilistic machine learning. Maybe that’s why I invited him on the show… Well, who knows, causal inference is very hard!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:Robert's webpage: https://www.microsoft.com/en-us/research/people/robertness/Robert on Twitter: https://twitter.com/osazuwaRobert on GitHub: https://github.com/robertnessRobert on LinkedIn: https://www.linkedin.com/in/osazuwa/Do-calculus enables causal reasoning with latent variable models, Arxiv: https://arxiv.org/abs/2102.06626Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems, NeurIPS Proceedings: https://proceedings.neurips.cc/paper/2019/hash/2d44e06a7038f2dd98f0f54c4be35e22-Abstract.htmlCausality 101 with Robert Ness, The TWIML AI Podcast: https://www.youtube.com/watch?v=UNEZztT5lpkCausal Modeling in Machine Learning, PyData Boston: https://www.youtube.com/watch?v=1BioSmE5m6sPyro -- Deep Universal Probabilistic Programming: http://pyro.ai/Statistical Rethinking website: http://xcelab.net/rm/statistical-rethinking/The Book of Why -- The New Science of Cause and Effect : https://www.goodreads.com/book/show/36204378-the-book-of-whyThe Theory That Would Not Die -- How Bayes' Rule Cracked the Enigma Code : https://www.goodreads.com/book/show/10672848-the-theory-that-would-not-die

What’s the common point between fiction, fake news, illusions and meditation? They can all be studied with Bayesian statistics, of course!In this mind-bending episode, Dominique Makowski will for sure expand your horizon. Trained as a clinical neuropsychologist, he is currently working as a postdoc at the Clinical Brain Lab in Singapore, in which he leads the Reality Bending Team. What’s reality-bending you ask? Well, you’ll have to listen to the episode, but I can already tell you we’ll go through a journey in scientific methodology, history of art, religion, and philosophy — what else?Beyond that, Dominique tries to improve the access to advanced analysis techniques by developing open-source software and tools, like the NeuroKit Python package or the bayestestR package in R.Even better, he looks a lot like his figures of reference. Like Marcus Aurelius, he plays the piano and guitar. Like Sisyphus, he loves history of art and comparative mythology. And like Yoda, he is a wakeboard master.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, 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, Daniel Lindroth, Yoshiyuki Hamajima, Sven De Maeyer and Michael DeCrescenzo.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:To follow:Dominique's website: https://dominiquemakowski.github.io/Dominique on Twitter: https://twitter.com/Dom_MakowskiDominique on GitHub: https://github.com/DominiqueMakowskiPackages:NeuroKit -- Python Toolbox for Neurophysiological Signal Processing: https://github.com/neuropsychology/NeuroKitbayestestR -- Become a Bayesian master you will: https://easystats.github.io/bayestestR/report -- From R to your manuscript: https://easystats.github.io/report/Research:The Reality Bending League :https://realitybending.github.io/research/What is Reality Bending: https://realitybending.github.io/post/2020-09-28-what_is_realitybending/Art:NeuropsyXart -- Neuroimaging methods to obtain visual representations of neurophysiological processes: https://dominiquemakowski.github.io/NeuropsyXart/

Let’s be honest: evolution is awesome! I started reading Improbable Destinies: Fate, Chance, and the Future of Evolution, by Jonathan Losos, and I’m utterly fascinated. So I’m thrilled to welcome Florian Hartig on the show. Florian is a professor of Theoretical Ecology at the University of Regensburg, Germany. His research concentrates on theory, computer simulations, statistical methods and machine learning in ecology & evolution. He is also interested in open science and open software development, and maintains, among other projects, the R packages DHARMa and BayesianTools.Among other things, we talked about approximate Bayesian computation, best practices when building models and the big pain points that remain in the Bayesian pipeline.Most importantly, Florian’s main hobbies are whitewater kayaking, snowboarding, badminton and playing the guitar.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, 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 and Daniel Lindroth.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Florian's website: https://theoreticalecology.wordpress.com/Florian on Twitter: https://twitter.com/florianhartigFlorian on GitHub: https://github.com/florianhartigDHARMa -- Residual Diagnostics for Hierarchical Regression Models: https://cran.r-project.org/web/packages/DHARMa/index.htmlBayesianTools -- General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics: https://cran.r-project.org/web/packages/BayesianTools/index.htmlStatistical inference for stochastic simulation inference -- theory and application: https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1461-0248.2011.01640.xArviZ plot rank function: https://arviz-devs.github.io/arviz/api/generated/arviz.plot_rank.htmlRank-normalization, folding, and localization -- An improved R-hat for assessing convergence of MCMC: https://arxiv.org/abs/1903.08008LBS #51 Bernoulli's Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://www.learnbayesstats.com/episode/51-bernoullis-fallacy-crisis-modern-science-aubrey-claytonLBS #50 Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://www.learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalterLBS #44 Building Bayesian Models at scale, with Rémi Louf: https://www.learnbayesstats.com/episode/44-bayesian-models-at-scale-remi-loufLBS #35 The Past, Present & Future of BRMS, with Paul Bürkner: https://www.learnbayesstats.com/episode/35-past-present-future-brms-paul-burknerLBS #29 Model Assessment, Non-Parametric Models, And Much More, with Aki Vehtari: https://www.learnbayesstats.com/episode/model-assessment-non-parametric-models-aki-vehtariImprobable Destinies -- Fate, Chance, and the Future of Evolution: https://www.goodreads.com/book/show/33357463-improbable-destinies