Learning Bayesian Statistics

Daniel Lee, an expert in Bayesian statistics, discusses the challenges in accurately depicting situations based on multiple data pieces. He explores identifying hidden objects, applying vision stats, building complex models in Bayesian statistics, and struggles in choosing priors.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meListen to the full episode: https://learnbayesstats.com/episode/96-pharma-models-sports-analytics-stan-news-daniel-lee/Watch the interview: https://www.youtube.com/watch?v=lnq5ZPlup0EVisit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !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,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, 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, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas and Luke Gorrie.

In this discussion, Daniel Lee shares his wealth of experience in numeric computation and sports analytics. He dives into advanced statistical models for oncology and their application in basketball and football. Daniel highlights his journey from pharma models to Zellus Analytics, while exploring the complexities of Bayesian statistics. He also talks about the latest developments in Stan and PyMC, offering insights on their impact in analytics. Additionally, he emphasizes the importance of data-driven decision-making, touching on social equity in sports analytics.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meWelcome to another installment of our LBS physics deep dive! After exploring the world of experimental physics at CERN in our first video documentary in episode 93, we’ll stay in Geneva for this one, but this time we’ll dive into theoretical physics.We’ll explore mysterious components of the universe, like dark matter and dark energy. We’ll also see how the study of gravity intersects with the study of particle physics, especially when considering black holes and the early universe. Even crazier, we’ll see that there are actual experiments and observational projects going on to answer these fundamental questions!Our guide for this episode is Valerie Domcke, permanent research staff member at CERN, who did her PhD in Hamburg, Germany, and postdocs in Trieste and Paris.When she’s not trying to decipher the mysteries of the universe, Valerie can be found on boats, as she’s a big sailing fan.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,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, 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, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls and Maksim Kuznecov.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Valerie’s webpage: https://theory.cern/roster/domcke-valerieValerie on Google Scholar: https://scholar.google.com/citations?user=E3g0tn4AAAAJLBS #93 A CERN Odyssey, with Kevin Greiff: https://www.youtube.com/watch?v=rOaqIIEtdpILBS #64, Modeling the Climate & Gravity Waves, with Laura Mansfield: https://learnbayesstats.com/episode/64-modeling-climate-gravity-waves-laura-mansfield/LBS Physics Playlist: https://learnbayesstats.com/physics-astrophysics/Abstractby Christoph BambergEpisode 95 is another instalment of our Deep Dive into Physics series. And this time we move away from the empirical side of this topic towards more theoretical questions. There is no one better for this topic than Dr. Valerie Domcke. Valerie is the second researcher from the CERN we have on our show. She is located at the Department of Theoretical Physics there.We mainly focus on the Standard Model of Physics, where it fails to explain observations, what proposals are discussed to update or replace it and what kind of evidence would be needed to make such a decision.Valerie is particularly interested in situations in which the Standard Model brakes down, such as when trying to explain the excess gravitational pull observed that cannot be accounted for by visible stars. Of course, we cover fascinating topics like dark matter, dark energy, black holes and gravitational waves that are places to look for evidence against the Standard Model.Looking more at the practical side of things, we discuss the challenges in disentangling signal from noise, especially in such complex fields as astro- and quantum-physics. We also touch upon the challenges Valerie is currently tackling in working on a new observatory for gravitational waves, the Laser Interferometer Space Antenna, LISA. TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

In this engaging discussion, Jonathan Templin, a Professor of Psychological and Quantitative Foundations at the University of Iowa, dives into the world of psychometrics and Bayesian statistics. He examines the significance of diagnostic classification models in psychological assessments and the challenges of choosing appropriate prior distributions. Jonathan shares his transformative journey into psychometrics and the evolution of Bayesian applications. The conversation also touches on the intricacies of model comparison and future trends in applying Bayesian methods across various disciplines.

Doctoral candidate in particle physics at UC Irvine, Kevin Greiff, takes a tour of the CERN campus with the host. They discuss topics such as unfolding in particle physics, muons, calorimeters, dark matter, limitations of the standard model, data storage challenges, antimatter, and Boltzmann's contributions to physics.

Gerd Gigerenzer, expert in decision-making under uncertainty, discusses topics such as choosing the appropriate statistical approach, the intersection between Bayesian thinking and heuristics, the need for statistical education, the effectiveness of teaching decision-making tools to doctors, and the admiration for Ada Lovelace and Marie Curie.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meAs you may know, I’m kind of a nerd. And I also love football — I've been a PSG fan since I’m 5 years old, so I’ve lived it all with this club.. And yet, I’ve never done a European-centered football analytics episode because, well, the US are much more advanced when it comes to sports analytics.But today, I’m happy to say this day has come: a sports analytics episode where we can actually talk about European football. And that is thanks to Maximilan Göbel.Max is a post-doctoral researcher in Economics and Finance at Bocconi University in Milan. Before that, he did his PhD in Economics at the Lisbon School of Economics and Management. Max is a very passionate football fan and played himself for almost 25 years in his local football club. Unfortunately, he had to give it up when starting his PhD — don’t worry, he still goes to the gym, or goes running and sometimes cycling.Max is also a great cook, inspired by all kinds of Italian food, and an avid podcast listener — from financial news, to health and fitness content, and even a mysterious and entertaining Bayesian podcast…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,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, 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, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau and Luis Fonseca.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Max’s website: https://www.maximiliangoebel.com/homeMax on GitHub: https://github.com/maxi-tb22Max on LinkedIn: https://www.linkedin.com/in/maximilian-g%C3%B6bel-188b0413a/Max’s Soccer Analytics page: https://www.maximiliangoebel.com/soccer-analyticsSoccer Factor Model on GitHub: https://github.com/maxi-tb22/SFMMax webinar on his Soccer Factor Model: https://www.youtube.com/watch?v=2dGrN8JGd_wMax's paper using Bayesian inference:VARCTIC - A Baysian Vector Autoregression for the Arctic: “Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis”: https://journals.ametsoc.org/view/journals/clim/34/13/JCLI-D-20-0324.1.xmlForecasting Arctic Sea Ice:Daily predictions of Arctic Sea Ice Extent: https://chairemacro.esg.uqam.ca/arctic-sea-ice-forecasting/?lang=enSea Ice Outlook (SIO) Forecasting competition: https://www.arcus.org/sipn/sea-ice-outlookSome of Max’s coauthors:Philippe Goulet Coulombe (UQAM): https://philippegouletcoulombe.com/Francis X. Diebold (UPenn): https://www.sas.upenn.edu/~fdiebold/Abstractby Christoph BambergWe already covered baseball analytics in the U.S.A. with Jim Albert in episode 85 and looked back at the decade long history of sports analytics there. How does it look like in Europe? To talk about this we got Max Göbel on the show. Max is a post-doctoral researcher in Economics and Finance at Bocconi University in Milan and holds a PhD in Economics from the Lisbon School of Economics and Management.What qualifies him to talk about the sports-side of sports analytics is his passion for football and decades of playing experience. So, can sports analytics in Europe compete with analytics in the U.S.A.? Unfortunately, not yet. Many sports clubs do not use models in their hiring decisions, leading to suboptimal choices based on players’ reputation alone, as Max explains.He designed a factor model for the performance of single players, borrowing from his econometrics expertise (check it out on his webpage, link in the show notes). We talk about how to grow this model from a simple and straight-forward Bernoulli model for the rate of scored goals to a multilevel model, incorporating other players. And of course, we discuss the benefits for using Bayesian statistics for this modelling problem.We also cover sport analytics more generally and why it may not be so widely used in European football clubs yet. Besides his interest in football analytics, Max worked and works on topics in econometrics such as regression forecasting in the U.S.A., asset pricing and applying econometric methods to climate change issues like climate change forecasting and sea ice disappearance.TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Charles Margossian, computational mathematician, discusses the differences between MCMC and Variational Inference (VI). They explore beginner questions, the practical applications of Bayesian methods in pharmacometrics and epidemiology, and the challenges of fitting mechanistic models in drug absorption and effects. They also touch on nested Laplace approximations and the complexity of Bayesian methods and data analysis.

Eric Trexler, a researcher at Duke University with a PhD in Human Movement Science, shares his insights on exercise, nutrition, and metabolism. He delves into metabolic adaptation and the complexities of weight management, explaining how caloric intake impacts energy expenditure. Trexler also highlights the role of Bayesian statistics in overcoming challenges in exercise science. The conversation touches on the connection between stoicism and dieting struggles, and the gap between scientific understanding and public misinformation.