Learning Bayesian Statistics

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Decision-making and cost effectiveness analyses rarely get as important as in the health systems — where matters of life and death are not a metaphor. Bayesian statistical modeling is extremely helpful in this field, with its ability to quantify uncertainty, include domain knowledge, and incorporate causal reasoning.Specialized in all these topics, Gianluca Baio was the person to talk to for this episode. He’ll tell us about this kind of models, and how to understand them.Gianluca is currently the head of the department of Statistical Science at University College London. He studied Statistics and Economics at the University of Florence (Italy), and completed a PhD in Applied Statistics, again at the beautiful University of Florence.He’s also a very skilled pizzaiolo — so now I have two reasons to come back to visit Tuscany…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, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, 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, 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, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, 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, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, and Arkady.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Gianluca’s website: https://gianluca.statistica.it/Gianluca on GitHub: https://github.com/giabaio Gianluca on Mastodon: https://mas.to/@gianlubaioGianluca on Twitter: https://twitter.com/gianlubaioGianluca on Linkedin: https://www.linkedin.com/in/gianluca-baio-b893879/Gianluca’s articles on arXiv: https://arxiv.org/a/baio_g_1.htmlR for Health Technology Assessment (HTA) Consortium: https://r-hta.org/ LBS #50 – Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalter/LBS #45 – Biostats & Clinical Trial Design, with Frank Harrell: https://learnbayesstats.com/episode/45-biostats-clinical-trial-design-frank-harrell/How to find priors intuitively= https://www.youtube.com/watch?v=9shZeqKG3M0Hierarchical Bayesian Modeling of Survey Data with Post-stratification: https://www.youtube.com/watch?v=efID35XUQ3ILBS Topical Playlists (also available as RSS feeds on the website): https://www.youtube.com/@learningbayesianstatistics8147/playlistsAbstractby Christoph BambergIn this week’s episode, I talk to Gianluca Baio. He is the head of the department of Statistical Science at University College London and earned a MA and PhD in Florence in Statistics and Economics.His work primarily focuses on Bayesian modeling for decision making in healthcare, for example in case studies for novel drugs and whether this alternative treatment is worth the cost. Being a relatively young field, health economics seems more open to Bayesian statistics than more established fields.While Bayesian statistics becomes more common in clinical trial research, many regulatory bodies still prefer classical p-values. Nonetheless, a lot of COVID modelling was done using Bayesian statistics.We also talk about the purpose of statistics, which is not to prove things but to reduce uncertainty.Gianluca explains that proper communication is important when eliciting priors and involving people in model building. The future of Bayesian statistics is that statistics should have more primacy, and he hopes that statistics will stay central rather than becoming embedded in other approaches like data science, notwithstanding, communication with other disciplines is crucial.TranscriptPlease note that the following transcript was generated automatically and may therefore contain errors. Feel free to reach out if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Matt Hoffman has already worked on many topics in his life – music information retrieval, speech enhancement, user behavior modeling, social network analysis, astronomy, you name it.Obviously, picking questions for him was hard, so we ended up talking more or less freely — which is one of my favorite types of episodes, to be honest.You’ll hear about the circumstances Matt would advise picking up Bayesian stats, generalized HMC, blocked samplers, why do the samplers he works on have food-based names, etc.In case you don’t know him, Matt is a research scientist at Google. Before that, he did a postdoc in the Columbia Stats department, working with Andrew Gelman, and a Ph.D at Princeton, working with David Blei and Perry Cook.Matt is probably best known for his work in approximate Bayesian inference algorithms, such as stochastic variational inference and the no-U-turn sampler, but he’s also worked on a wide range of applications, and contributed to software such as Stan and TensorFlow Probability.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, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, 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, 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, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, 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, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode and Gabriel Stechschulte.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Matt’s website: http://matthewdhoffman.com/Matt on Google Scholar: https://scholar.google.com/citations?hl=en&user=IeHKeGYAAAAJ&view_op=list_worksThe No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo: https://www.jmlr.org/papers/volume15/hoffman14a/hoffman14a.pdfTuning-Free Generalized Hamiltonian Monte Carlo: https://proceedings.mlr.press/v151/hoffman22a/hoffman22a.pdfNested R-hat: Assessing the convergence of Markov chain Monte Carlo when running many short chain: http://www.stat.columbia.edu/~gelman/research/unpublished/nestedRhat.pdfAutomatic Reparameterisation of Probabilistic Programs: http://proceedings.mlr.press/v119/gorinova20a/gorinova20a.pdfAbstractwritten by Christoph BambergIn this episode, Matt D. Hoffman, a Google research scientist discussed his work on probabilistic sampling algorithms with me. Matt has a background in music information retrieval, speech enhancement, user behavior modeling, social network analysis, and astronomy. He came to machine learning (ML) and computer science through his interest in synthetic music and later took a Bayesian modeling class during his PhD. He mostly works on algorithms, including Markov Chain Monte Carlo (MCMC) methods that can take advantage of hardware acceleration, believing that running many small chains in parallel is better for handling autocorrelation than running a few longer chains. Matt is interested in Bayesian neural networks but is also skeptical about their use in practice. He recently contributed to a generalised Hamilton Monte Carlo (HMC) sampler, and previously worked on an alternative to the No-U-Turn-Sampler (NUTS) called MEADS. We discuss the applications for these samplers and how they differ from one another. In addition, Matt introduces an improved R-hat diagnostic tool, nested R-hat, that he and colleagues developed. Automated TranscriptPlease note that the following transcript was generated automatically and may therefore contain errors. Feel free to reach out if you’re willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!I love dresses. Not on me, of course — I’m not nearly elegant enough to pull it off. Nevertheless, to me, dresses are one of the most elegant pieces of clothing ever invented.And I like them even more when they change colors. Well, they don’t really change colors — it’s the way we perceive the colors that can change. You remember that dress that looked black and blue to some people, and white and gold to others? Well that’s exactly what we’ll dive into and explain in this episode.Why do we literally see the world differently? Why does that even happen beyond our consciousness, most of the time? And cherry on the cake: how on Earth could this be related to… priors?? Yes, as in Bayesian priors!Pascal Wallisch will shed light on all these topics in this episode. Pascal is a professor of Psychology and Data Science at New York University, where he studies a diverse range of topics including perception, cognitive diversity, the roots of disagreement and psychopathy.Originally from Germany, Pascal did his undergraduate studies at the Free University of Berlin. He then received his PhD from the University of Chicago, where he studied visual perception.In addition to scientific articles on psychology and neuroscience, he wrote multiple books on scientific computing and data science. As you’ll hear, Pascal is a wonderful science communicator, so it's only normal that he also writes for a general audience at Slate or the Creativity Post, and has given public talks at TedX and Think and Drink.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, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, 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, 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, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, 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, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R and Nicolas Rode.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Pascal’s website: https://blog.pascallisch.net/about/Pascal on Twitter: https://twitter.com/pascallischPascal on Linkedin: https://www.linkedin.com/in/pascal-wallisch-0109b77“Socks & Crocs”, You Are Not So Smart podcast, Episode 200: https://youarenotsosmart.com/2021/02/22/yanss-200-how-a-divisive-photograph-of-a-perceptually-ambiguous-dress-led-two-researchers-to-build-the-nuclear-bomb-of-cognitive-science-out-of-socks-and-crocs/You Are Not So Smart – Live in New York at The Bell House: https://www.youtube.com/watch?v=277HGgqrrUM&t=1sPascal’s paper – Illumination assumptions account for individual differences in the perceptual interpretation of a profoundly ambiguous stimulus in the color domain: https://jov.arvojournals.org/article.aspx?articleid=2617976 Neural Data Science – A Primer with MATLAB and Python: https://www.amazon.com/Neural-Data-Science-MATLAB%C2%AE-PythonTM/dp/0128040432What Color Is The Dress? The Debate That Broke The Internet: https://www.nhpr.org/2015-02-27/what-color-is-the-dress-the-debate-that-broke-the-internet#stream/0The inside story of the ‘white dress, blue dress’ drama that divided a planet: https://www.washingtonpost.com/news/morning-mix/wp/2015/02/27/the-inside-story-of-the-white-dress-blue-dress-drama-that-divided-a-nation/Noise characteristics and prior expectations in human visual speed perception: https://www.nature.com/articles/nn1669Bayesian integration in sensorimotor learning: https://www.nature.com/articles/nature02169Abstractby Christoph BambergIn our conversation, Pascal Wallisch, a professor of Psychology and Data Science at New York University, shared about his research on perception, cognitive diversity, the roots of disagreement, and psychopathy. Pascal did his undergraduate studies at the Free University of Berlin and then received his PhD from the University of Chicago, where he studied visual perception. Pascal is also a TedX, Think and Drink speaker, and writer for Slate and Creativity Post. We discussed Pascal's origin story, his current work on cognitive diversity, and the importance of priors in perception. Pascal used the example of "the Dress" picture that went viral in 2015, where people saw either black and blue or white and gold. He explained how prior experience and knowledge can affect how people perceive colors and motion, and how priors can bias people for action. We discussed to what extent the brain might be Bayesian and what functions are probably not so well described in bayesian terms. Pascal also discussed how priors can be changed through experience and exposure.Finally, Pascal emphasized that people have different priors and perspectives, and that understanding these differences is crucial for creating a more diverse and inclusive society.Automated TranscriptPlease note that the following transcript was generated automatically and may therefore contain errors. Feel free to reach out if you’re willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!How does it feel to switch careers and start a postdoc at age 47? How was it to be one of the people who created the probabilistic programming language Stan? What should the Bayesian community focus on in the coming years?These are just a few of the questions I had for my illustrious guest in this episode — Bob Carpenter. Bob is, of course, a Stan developer, and comes from a math background, with an emphasis on logic and computer science theory. He then did his PhD in cognitive and computer sciences, at the University of Edinburgh.He moved from a professor position at Carnegie Mellon to industry research at Bell Labs, to working with Andrew Gelman and Matt Hoffman at Columbia University. Since 2020, he's been working at Flatiron Institute, a non-profit focused on algorithms and software for science.In his free time, Bob loves to cook, see live music, and play role playing games — think Monster of the Week, Blades in Dark, and Fate.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, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bert≈rand 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, 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, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, 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, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin and Raphaël R.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Bob’s website: https://bob-carpenter.github.ioBob on GitHub: https://github.com/bob-carpenterBob on Google Scholar: https://scholar.google.com.au/citations?user=kPtKWAwAAAAJ&hl=enStat modeling blog: https://statmodeling.stat.columbia.eduStan home page: https://mc-stan.org/BridgeStan home page: https://github.com/roualdes/bridgestanbayes-infer home page: https://github.com/bob-carpenter/bayes-inferCrowdsourcing with item difficulty: https://github.com/bob-carpenter/rater-difficulty-paperPathfinder VI system: https://www.jmlr.org/papers/v23/21-0889.htmlFlatiron Institute home page: https://www.simonsfoundation.org/flatiron/0 to 100K in 10 years – Nurturing an open-source software community: https://www.youtube.com/watch?v=P9gDFHl-Hss&t=81sInformation Theory, Inference and Learning Algorithms: https://www.amazon.com/Information-Theory-Inference-Learning-Algorithms/dp/0521642981LBS #20 – Regression and Other Stories, with Andrew Gelman, Jennifer Hill & Aki Vehtari: https://learnbayesstats.com/episode/20-regression-and-other-stories-with-andrew-gelman-jennifer-hill-aki-vehtari/LBS #27 – Modeling the US Presidential Elections, with Andrew Gelman & Merlin Heidemanns: https://learnbayesstats.com/episode/27-modeling-the-us-presidential-elections-with-andrew-gelman-merlin-heidemanns/LBS #17 – Reparametrize Your Models Automatically, with Maria Gorinova: https://learnbayesstats.com/episode/17-reparametrize-your-models-automatically-with-maria-gorinova/LBS #36 – Bayesian Non-Parametrics & Developing Turing.jl, with Martin Trapp: https://learnbayesstats.com/episode/36-bayesian-non-parametrics-developing-turing-julia-martin-trapp/LBS #19 – Turing, Julia and Bayes in Economics, with Cameron Pfiffer: https://learnbayesstats.com/episode/19-turing-julia-and-bayes-in-economics-with-cameron-pfiffer/LBS #74 – Optimizing NUTS and Developing the ZeroSumNormal Distribution, with Adrian Seyboldt: https://learnbayesstats.com/episode/74-optimizing-nuts-developing-zerosumnormal-distribution-adrian-seyboldt/Bayesian Workflow paper: https://arxiv.org/abs/2011.01808BAyesian Model-Building Interface (Bambi) in Python: https://bambinos.github.io/bambi/On Being Certain: Believing You Are Right Even When You're Not: https://www.amazon.com/Being-Certain-Believing-Right-Youre/dp/031254152XAbstractby Christoph BambergIn this episode, you meet the man behind the code. Namely, Bob Carpenter, one of the core developers of STAN, a popular statistical programming language. After working in computational linguistic for some time, Bob became a PostDoc with Andrew Gellman to really learn Statistics and Modelling.There he and a small team developed the first implementation of STAN. We talk about the challenges associated with the team growing and the Open Source conventions. Besides the initial intention behind and the beginning of STAN, we talk about the future of probabilistic programming.Creating a tool for people with different degrees of mathematics and programming knowledge is a big challenge and working with these tools may also be more difficult for the user.We discuss why Bayesian statistical programming is popular nonetheless and what makes it uniquely adequate for research.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!If you’re a nerd like me, you’re always curious about the physics of any situation. So, obviously, when I watched Top Gun 2, I became fascinated by the aerodynamics of fighters jets. And it so happens that one of my friends used to be a fighter pilot for the Canadian army… Immediately, I thought this would make for a cool episode — and here we are!Actually, Jason Berndt wanted to be a pilot from the age of 3. When he was 6, he went to an air show, and then specifically wanted to become a fighter pilot. In his teens, he learned how to fly saliplanes, small single engine aircrafts. At age 22, he got a bachelor’s in aero engineering from the royal military college, and then — well, he’ll tell you the rest in the episode.Now in his thirties, he owns real estate and created his own company, My Two Brows, selling temporary eyebrow tattoos — which, weirdly enough, is actually related to his time in the army…In his free time, Jason plays the guitar, travels around the world (that’s actually how we met), and loves chasing adrenaline however he can (paragliding, scuba diving, you name 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, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bert≈rand 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, 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, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, 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, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin and Raphaël R.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:My Two Brows website: https://mytwobrows.com/My Two Brows on Instagram: https://www.instagram.com/my_two_brows/My Two Brows on YouTube: https://www.youtube.com/channel/UC6eQgQ4qoGE2RStDJkumUGgPyMC Labs Workshop – Hierarchical Bayesian Modeling of Survey Data with Post-stratification: https://www.youtube.com/watch?v=efID35XUQ3IAbstractwritten by Christoph BambergIn this episode of the Learning bayesian statistics podcast we do not talk about Bayesianism, let alone statistics. Instead we dive into the world of fighter jets and Top Gun pilots with Jason Berndt. Jason is a former fighter jet pilot turned entrepreneur. He looks back at his time as a pilot, how he got there, the challenges and thrills of this job and how it influences him now in his new life. We also touch upon physics and science related aspects like G-force, centrifugal power, automation in critical environments like flying a fighter jet and human-computer interaction.Jason discusses the recent movie Top Gun: Maverick and how realistic the flying was as well as the description of the fighter pilots’ lives.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!We need to talk. I had trouble writing this introduction. Not because I didn’t know what to say (that’s hardly ever an issue for me), but because a conversation with Adrian Seyboldt always takes deliciously unexpected turns.Adrian is one of the most brilliant, interesting and open-minded person I know. It turns out he’s courageous too: although he’s not a fan of public speaking, he accepted my invitation on this show — and I’m really glad he did!Adrian studied math and bioinformatics in Germany and now lives in the US, where he enjoys doing maths, baking bread and hiking.We talked about the why and how of his new project, Nutpie, a more efficient implementation of the NUTS sampler in Rust. We also dived deep into the new ZeroSumNormal distribution he created and that’s available from PyMC 4.2 onwards — what is it? Why would you use it? And when?Adrian will also tell us about his favorite type of models, as well as what he currently sees as the biggest hurdles in the Bayesian workflow.Each time I talk with Adrian, I learn a lot and am filled with enthusiasm — and now I hope 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/ !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, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bert≈rand 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, 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, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, 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, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey and Andreas Kröpelin.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:LBS on Twitter: https://twitter.com/LearnBayesStatsLBS on Linkedin: https://www.linkedin.com/company/learn-bayes-stats/Adrian on GitHub: https://github.com/aseyboldtNutpie repository: https://github.com/pymc-devs/nutpieZeroSumNormal distribution: https://www.pymc.io/projects/docs/en/stable/api/distributions/generated/pymc.ZeroSumNormal.htmlPathfinder – A parallel quasi-Newton algorithm for reaching regions of high probability mass: https://statmodeling.stat.columbia.edu/2021/08/10/pathfinder-a-parallel-quasi-newton-algorithm-for-reaching-regions-of-high-probability-mass/Abstractby Christoph BambergAdrian Seyboldt, the guest of this week’s episode, is an active developer of the PyMC library in Python and his new tool nutpie in Rust. He is also a colleague at PyMC-Labs and friend. So naturally, this episode gets technical and nerdy. We talk about parametrisation, a topic important for anyone trying to implement a Bayesian model and what to do or avoid (don't use the mean of the data!). Adrian explains a new approach to setting categorical parameters, using the Zero Sum Normal Distribution that he developed. The approach is explained in an accessible way with examples, so everyone can understand and implement it themselves.We also talked about further technical topics like initialising a sampler, the use of warm-up samples, mass matrix adaptation and much more. The difference between probability theory and statistics as well as his view on the challenges in Bayesian statistics complete the episode. 

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!I’m guessing you already tried to communicate the results of a statistical model to non-stats people — it’s hard, right? I’ll be honest: sometimes, I even prefer to take notes during meetings than doing that… But shhh, that’s out secret.But all of this was before. Before I talked with Jessica Hullman. Jessica is the Ginny Rometty associate professor of computer science at Northwestern University.Her work revolves around how to design interfaces to help people draw inductive inferences from data. Her research has explored how to best align data-driven interfaces and representations of uncertainty with human reasoning capabilities, which is what we’ll mainly talk about in this episode.Jessica also tries to understand the role of interactive analysis across different stages of a statistical workflow, and how to evaluate data visualization interfaces.Her work has been awarded with multiple best paper and honorable mention awards, and she frequently speaks and blogs on topics related to visualization and reasoning about uncertainty — as usual, you’ll find the links 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/ !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, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bert≈rand 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, 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, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, 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, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox and Trey Causey.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)General links from the show:Jessica’s website: http://users.eecs.northwestern.edu/~jhullman/ Jessica on Twitter: https://twitter.com/JessicaHullmanMidwest Uncertainty Collective: https://mucollective.northwestern.edu/Jessica’s posts on Andrew Gelman’s blog: https://statmodeling.stat.columbia.edu/Jessica’s posts on Medium: https://medium.com/multiple-views-visualization-research-explainedLBS # 66, Uncertainty Visualization & Usable Stats, with Matthew Kay: https://learnbayesstats.com/episode/66-uncertainty-visualization-usable-stats-matthew-kay/Some of Jessica’s research that she mentioned:A Bayesian Model of Cognition to Improve Data Visualization: https://mucollective.northwestern.edu/files/2019-BayesianVis-CHI.pdfVisual Reasoning Strategies for Effect Size Judgments and Decisions: https://mucollective.northwestern.edu/files/2020%20-%20Kale,%20Visual%20Reasoning%20Strategies%20for%20Effect%20Size%20Judgements.pdfHypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data: https://mucollective.northwestern.edu/files/2018-HOPsTrends-InfoVis.pdfBehavioral economics paper Jessica mentioned:A Model of Non-belief in the Law of Large Numbers: https://scholar.harvard.edu/files/rabin/files/barney2014.pdfMore on David Blackwell:Summary of his career: https://stat.illinois.edu/news/2020-07-17/david-h-blackwell-profile-inspiration-and-perseveranceHis original work on Blackwell ordering: https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-24/issue-2/Equivalent-Comparisons-of-Experiments/10.1214/aoms/1177729032.pdfLectures on day 5 of this workshop covered his work on approachability: https://old.simons.berkeley.edu/workshops/schedule/16924Abstract:by Christoph BambergProfessor Jessica Hullman from Northwestern University is an expert in designing visualisations that help people learn from data and not fall prey to biases.She focuses on the proper communication of uncertainty, both theoretically and empirically.She addresses questions like “Can a Bayesian model of reasoning explain apparently biased reasoning?”, “What kind of visualisation guides readers best to a valid inference?”, “How can biased reasoning be so prevalent - are there scenarios where not following the canonical reasoning steps is optimal?”.In this episode we talk about her experimental studies on communication of uncertainty through visualisation, in what scenarios it may not be optimal to focus too much on uncertainty and how we can design models of reasoning that can explain actual behaviour and not discard it as biased. 

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!What happens inside a black hole? Can we travel back in time? Why is the Universe even here? This is the type of chill questions that we’re all asking ourselves from time to time — you know, when we’re sitting on the beach.This is also the kind of questions Daniel Whiteson loves to talk about in his podcast, “Daniel and Jorge Explain the Universe”, co-hosted with Jorge Cham, the author of PhD comics. Honestly, it’s one of my favorite shows ever, so I warmly recommend it. Actually, if you’ve ever hung out with me in person, there is a high chance I started nerding out about it…Daniel is, of course, a professor of physics, at the University of California, Irvine, and also a researcher at CERN, using the Large Hadron Collider to search for exotic new particles — yes, these are particles that put little umbrellas in their drinks and taste like coconut.On his free time, Daniel loves reading, sailing and baking — I can confirm that he makes a killer Nutella roll!Oh, I almost forgot: Daniel and Jorge wrote two books — We Have No Idea and FAQ about the Universe — which, again, I strongly recommend. They are among my all-time favorites.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, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bert≈rand 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, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, 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, Robert Yolken, Or Duek, Pavel Dusek and Paul Cox.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:PyMC Labs Meetup, Dec 8th 2022, A Candle in the Dark – How to Use Hierarchical Post-Stratification with Noisy Data: https://www.meetup.com/pymc-labs-online-meetup/events/289949398/Daniel’s website: https://sites.uci.edu/daniel/Daniel on Twitter: https://twitter.com/DanielWhiteson“Daniel and Jorge Explain the Universe”: https://sites.uci.edu/danielandjorge/?pname=danielandjorge.com&sc=dnsredirectWe Have No Idea – A Guide To The Unknown Universe: https://phdcomics.com/noidea/Frequently Asked Questions About The Universe: https://sites.uci.edu/universefaq/Learning to Identify Semi-Visible Jets: https://arxiv.org/abs/2208.10062Twitter thread about the paper above: https://twitter.com/DanielWhiteson/status/1561929005653057536Abstractby Christoph BambergBig questions are tackled in episode 72 of the Learning Bayesian Statistics Podcast: “What is the nature of the universe?”, “What is the role of science?”, “How are findings in physics created and communicated?”, “What is randomness actually?”. This episode’s guest, Daniel Whitesun, is just the right person to address these questions.He is well-known for his own podcast “Daniel and Jorge Explain the Universe”, wrote several popular science books on physics and works as a particle physicist with data from the particle physics laboratory CERN.He manages to make sense of Astrology, although he is not much of a star-gazer himself. Daniel prefers to look for weird stuff in the data of colliding particles and ask unexpected questions.This comes with great statistical challenges that he tackles with Bayesian statistics and machine learning, while he also subscribes to the frequentist philosophy of statistics.In the episode, Alex and Daniel touch upon many of the great ideas in quantum physics, the Higgs boson, Schrödinger’s cat, John Bell’s quantum entanglement discoveries, true random processes and much more. Mixed in throughout are pieces of advice for anyone scientifically-minded and curious about the universe.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!This episode will show you different sides of the tech world. The one where you research and apply algorithms, where you get super excited about image recognition and AI-generated art. And the one where you support social change actors — aka the “AI for Good” movement.My guest for this episode is, quite naturally, Julien Cornebise. Julien is an Honorary Associate Professor at UCL. He was an early researcher at DeepMind where he designed its early algorithms. He then worked as a Director of Research at ElementAI, where he built and led the London office and “AI for Good” unit.After his theoretical work on Bayesian methods, he had the privilege to work with the NHS to diagnose eye diseases; with Amnesty International to quantify abuse on Twitter and find destroyed villages in Darfur; with Forensic Architecture to identify teargas canisters used against civilians.Other than that, Julien is an avid reader, and loves dark humor and picking up his son from school at the 'hour of the daddies and the mommies”.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, 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, 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, Robert Yolken, Or Duek, Pavel Dusek and Paul Cox.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Julien’s website: https://cornebise.com/julien/Julien on Twitter: https://twitter.com/JCornebiseJulien on LinkedIn: https://www.linkedin.com/in/juliencornebise/ Julien on Scholar: https://scholar.google.co.uk/citations?user=6fkVVz4AAAAJ&hl=en&oi=aoStable Diffusion is a really big deal: https://simonwillison.net/2022/Aug/29/stable-diffusion/LBS #21, Gaussian Processes, Bayesian Neural Nets & SIR Models, with Elizaveta Semenova: https://learnbayesstats.com/episode/21-gaussian-processes-bayesian-neural-nets-sir-models-with-elizaveta-semenova/pymc.find_constrained_prior function: https://www.pymc.io/projects/docs/en/stable/api/generated/pymc.find_constrained_prior.html#pymc.find_constrained_priorLBS #50, Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalter/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/Abstractby Christoph BambergJulien Cornebise goes on a deep dive into deep learning with us in episode 71. He calls himself a “passionate, impact-driven scientist in Machine Learning and Artificial Intelligence”. He holds an Honorary Associate Professor position at UCL, was an early researcher at DeepMind, went on to become Director of Research at ElementAI and worked with institutions ranging from the NHS in Great-Britain to Amnesty International. He is a strong advocate for using Artificial Intelligence and computer engineering tools for good and cautions us to think carefully about who we develop models and tools for. Ask the question: What could go wrong? How could this be misused? The list of projects where he used his computing skills for good is long and divers: With the NHS he developed methods to measure and diagnose eye diseases. For Amnesty International he helped quantify the abuse female journalists receive on Twitter, based on a database of tweets labeled by volunteers. Beyond these applied projects, Julien and Alex muse about the future of structured models in times of more and more popular deep learning approaches and the fascinating potential of these new approaches. He advices anyone interested in these topics to be comfortable with experimenting by themselves and potentially breaking things in a non-consequential environment. And don’t be too intimidated by more seasoned professionals, he adds, because they probably have imposter-syndrome themselves which is a sign of being aware of ones own limitations. Automated TranscriptPlease note that the following transcript was generated automatically and may therefore contain errors. Feel free to reach out if you’re willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Back in 2016, when I started dedicating my evenings and weekends to learning how to code and do serious stats, I was a bit lost… Where do I start? Which language do I pick? Why are all those languages just named with one single letter??Then I found some stats classes by Justin Bois — and it was a tremendous help to get out of that wood (and yes, this was a pun). I really loved Justin’s teaching because he was making the assumptions explicit, and also explained them — which was so much more satisfying to my nerdy brain, which always wonders why we’re making this assumption and not that one.So of course, I’m thrilled to be hosting Justin on the show today! Justin is a Teaching Professor in the Division of Biology and Biological Engineering at Caltech, California, where he also did his PhD. Before that, he was a postdoc in biochemistry at UCLA, as well as the Max Planck Institute in Dresden, Germany.Most importantly for the football fans, he’s a goalkeeper — actually, the day before recording, he saved two penalty kicks… and even scored a goal! A big fan of Los Angeles football club, Justin is a also a magic enthusiast — he is indeed a member of the Magic Castle…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, Robert Yolken and Or Duek.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Justin’s website: http://bois.caltech.edu/index.html Justin on GitHub: https://github.com/justinbois/Justin’s course on Data analysis with frequentist inference: https://bebi103a.github.io/Justin’s course on Bayesian inference: https://bebi103b.github.io/LBS #6, A principled Bayesian workflow, with Michael Betancourt:  https://learnbayesstats.com/episode/6-a-principled-bayesian-workflow-with-michael-betancourt/Physical Biology of the Cell: https://www.routledge.com/Physical-Biology-of-the-Cell/Phillips-Kondev-Theriot-Garcia-Phillips-Kondev-Theriot-Garcia/p/book/9780815344506Knowledge Illusion – Why We Never Think Alone: https://www.amazon.fr/Knowledge-Illusion-Never-Think-Alone/dp/039918435XTheSustainable Energy – Without the Hot Air: https://www.amazon.com/Sustainable-Energy-Without-Hot-Air/dp/0954452933Information Theory, Inference and Learning Algorithms: https://www.amazon.com/Information-Theory-Inference-Learning-Algorithms/dp/0521642981AbstractBy Christoph BambergJustin Bois did his Bachelor and PhD in Chemical Engineering before working as a Postdoctoral Researcher in Biological Physics, Chemistry and Biological Engineering. He now works as a Teaching Professor at the division of Biology and Biological Engineering at Caltech, USA. He first got into Bayesian Statistics like many scientists in fields like biology or psychology, by wanting to understand what the statistics actually mean that he was using. His central question was “what is parameter estimation actually?”. After all, that’s a lot of what doing quantitative science is on a daily basis! The Bayesian framework allowed him to find an answer and made him feel like a more complete scientist. As a teaching professor, he is now helping students of life sciences such as neuroscience or biological engineering to become true Bayesians. His teaching covers what you need to become a proficient Bayesian analyst, from opening datasets to Bayesian inference. He emphasizes the importance of models implicit in quantitative research and shows that we do in most cases have a prior idea of an estimand’s magnitude. Justin believes that we are naturally programmed to think in a Bayesian framework but still should mess up sometimes to learn that statistical techniques are fragile. You can find some of his teaching on his website.TranscriptThis transcript was generated automatically. Some transcription errors may have remained. Feel free to reach out if you're willing to correct them.[00:00:00] In 2016, when I started dedicating my evenings and weekends to learning how to code and do serious stats, I was a bit lost, to be honest. Where do I start? Which language do I speak? Why are all those languages just named with one single letter, like R or C? Then I found some stats classes by just in voice.And it was a tremendous help to get out of that wood. And yes, this was a pun. I really enjoyed Justine's teaching because he was making the assumptions explicit, and he also explained them, which was so much more satisfying to my minority brain, which always wonders why we're making this assumption and not that one.So of course, I'm thrilled to be hosting Justin on the show today. Justin is a teaching professor in the division of biology and biological engineering at Caltech, California, where he also did his PhD. Before that, he was a postdoc in biochemistry at UCLA as well as the Max Plan Institute in Tris, Germany.Most importantly, for the football fans, Justin is a goalkeeper. [00:01:00] Actually, the day before recording, he saved two penalty, penalty, kicks, and even scored a goal. Yes, a big fan of Los Angeles's football club. Justine is also a magic enthusiast. He is indeed a member of the Magic Castle. This is Learning Patient Statistics.Ex episode 70, recorded September 2nd, 2022. Welcome to Learning Patient Statistics, a fortnightly podcast on Beijing Inference, The methods project in the People who Make Impossible. I'm your host, Alex Andora. You can follow me Twitter at ann underscore like the country. For any info about the podcast, learn base stats.com is lap less to be Show notes becoming corporate sponsor supporting lbs and Pat.Unlocking base merge, everything is in there. That's learn base dance.com. If with all that info, a model is still resisting you, or if you find my voice special, smooth and [00:02:00] want me to come and teach patient stats in company, then reach out at alex.andorra@pymc-labs.io or book call with me at learnbayesstats.com.Thanks a lot folks. And best patient wish shes to you old. Let me show you how to be a good bla and change your predictions after taking information and, and if you're thinking they'll be less than amazing, let's adjust those expectations. What's a basian is someone who cares about evidence and doesn't jump to assumptions based on intuitions and prejudice.Abassian makes predictions on the best available info and adjusts the probability cuz every belief is provisional. And when I kick a flow, mostly I'm watching eyes widen. Maybe cuz my likeness lowers expectations of tight ryman. How would I know unless I'm Ryman in front of a bunch of blind men, drop in placebo controlled science like I'm Richard Feinman, just in boys.Welcome to Learning Patient St Sticks. Thank you. Happy to be here. Yes. Very [00:03:00] happy to have you here because, well, you know that, but listeners do not. But you are actually one of the first people who introduced me back to, uh, statistics and programming in 2017 when I started my Carrie Shift. So it's awesome to have you here today.I'm glad my stuff helped you get going. That's, that's the point. That's the goal. Yeah. Yeah, that's really cool. And also, I'm happy to have learned how you pronounce your last name because in French, you know, that's a French name. I dunno if you have some French origin, but in French it means, I know, I know it's a French name, but it's actually, as far as I understand, my family's from Northern Germany and there's a, a name there that's spelled b e u s s, like, and it's pronounced like in Germany, you say Boce.And then it got anglicized, I think when I moved to the US but uh, I was actually recently, just this past summer in Luanne, Switzerland, and there was a giant wood recycling bin. With my name on it, , it said d i s. So I got my picture taken next to that. So yeah. Yeah. Lo Zen is in the French speaking part of Switzerland.[00:04:00] That's right. Cool. So we're starting already with the origin story, so I love that cuz it's actually always my first question. So how did you jump to the stats in biology worlds and like how Senior of a Pass read it? Well, I think the path that I had toward really thinking carefully about statistical inferences is a very common path among scientists, meaning scientists outside of data scientists and, and maybe also outside of really data rich branches of sciences such as astronomy.So I studied chemical engineering as an undergraduate. It was a standard program. I didn't really do any undergrad research or anything, but I got into a little bit of statistics when I had a job at Kraft Foods. After undergraduate where I worked at the statistician on doing some predictive modeling about, uh, some food safety issues.And I thought it was interesting, but I sort of just, I was an engineer. I was making the product, I was implementing the stuff in the production facility and the statistician kind of took care of [00:05:00] everything else. I thought, I thought he was one of the coolest people in the company, . Um, but I didn't really, you know, it didn't really hook me in to really thinking about that.But I went and did a PhD and my PhD really didn't involve really much experimentation at all. I was actually doing computational modeling of like how nucleic acids get their structure and shape and things. And that was, it just didn't really involve analysis of much data. Then in my post-doctoral studies, in my post-doctoral work, I was working with some experimentalists who had some data sets and they needed.do estimates of parameters based on some theoretical models that I had derived or worked on. And I had done some stuff and you know, various lab classes and stuff, but it's your standard thing. It's like, ooh, I know how to do a cur fit. Meaning I can, I guess in the Python way I would do it, SciPi dot optimized dot cur fit.Or you know, in MATLAB I could do at least squares or something like that. And, and I knew this idea of minimizing the sum of the square of the residuals and that's gonna get you [00:06:00] a line that looks close to what your data points are. But the inference problems, the theoretical curves were actually a little bit say for some of 'em.There was no close to form solution. They were actually solutions to differential equations. And so the actual theoretical treatment I had was a little bit more complicated. And so I needed to start to think a little bit more carefully about exactly how we're going about estimating the parameters thereof.Right? And so I kind of just started grabbing uh, books and I. Discovered quickly that I had no idea what I was doing, , and actually neither did anybody around me. And I don't mean that pejoratively, it's just, it's a very common thing among the scient. A lot of people in the sciences that aren't, that don't work as much with data.And perhaps it's less common now, but it's definitely more common than, you know, 10, 15, uh, years ago. And so I just kind of started looking into how we should actually think about the estimates of [00:07:00] parameters given a data set. And really what happened was the problem became crystallized for me, the problem of parameter estimation.And I had never actually heard that phrase, perimeter estimation. To me. It was find the best fit per. If your curve goes through your data point, that means that you're, the theory that you derived is probably pretty good. And of course, I didn't think about what the word probably meant there. I, I only knew it colloquially, right?And so, cuz I was focused on deriving what the theory is. And of course that's a whole, hugely important part of, of the scientific enterprise. But once you get that theory arrived to try to estimate the parameters of that are present in that theory from measurement, that problem just became clear to me.Once I had a clear problem statement, then I was able to start to think about how to solve it. And so the problem statement was, I have a theory that has a set of parameters. I want to try to figure out what the parameters are by taking [00:08:00] some measurements and checking for one set of parameters. The measurements would be different.How do I find what parameters there are to, to give me this type, type of data that I observe. I intentionally just stated that awkwardly because that awkwardness there sort of made the, It's funny, it made it clear to me that the problem was unclear . And, and so I, that's what got me into a basian mode of thinking because it was hard for me to wrap my head around what it meant to do that thing that I've been doing all this time.This minimizing some squares of residuals and trying to find the best fit parameter. And, you know, in retrospect now I've actually, you know, that I taught myself. Cause I didn't really ever have a course in statistical inference or anything like that, say Okay. I was essentially doing a maximum likelihood estimation, which is a f way of doing prime destination.And I, and I hadn't actually thought about what that meant. I mean, I understand that now. We don't really need to talk [00:09:00] about that since we're talking about BA stuff now, but, and it was just harder for me to wrap my head around what that meant. And so I started reading. About the basing interpretation of probability, and it was really, it really just crystallized everything and made it clear, and then I could state the problem much more clearly.The problem was I was trying to find a posterior probability density function for these parameters given the data, and that was just so much clearly stated in Baying framework, and then that kinda lit me on fire because I was like, Holy cow, this thing that we do so often in the scientific enterprise, I can actually state the question , right?And I just thought that was such a profound moment, and then I was kind of hooked from there on out and I, I was concent trying to improve how I thought about these things. And yeah, so I did a lot of reading. I realized I just talked a lot. You probably have [00:10:00] some questions about some of the stuff I just said, so please.Oh yeah, well wait. But, um, I mean, that's good to have a, an overview like that. And so I guess that's also like, it sounds like you were introduced to patient statistics at the same time as you were doing that deep dive into, wait, like, I'm not sure I understand what I'm using then. Oh, actually I don't understand anything and then I have to learn about that.But it seems that you, you were also introduced to patient stats at that same time, Is that right? Yeah, I think so. And I think this is actually sort of a classic way in which scientists come up with what it is that they want to study. Because instead you start poking around, you kind of don't really know where the holes in your knowledge are.And so what I saw was like just a giant hole in my knowledge and my toolbox, and I saw the hole and I said, All right, let's fill it . And um, and so then I just started feeling around on how to do that. I see. And I am also curious as [00:11:00] to, and what motivated you to dive into the Beijing way of doing things?I really do think it was the clarity. I think that, Okay. I think that arguing about like what interpretation or probability you wanna use is not the most fruitful way to spend one's time. For me, it was really, it was just so much more intuitive. I felt like I could have this interpretation of probability that it's, it's a quantification of the plausibility of a logical conjecture of any logical conjecture gave me sort of the flexibility where I could think about like a...