#96 Pharma Models, Sports Analytics & Stan News, with Daniel Lee
Learning Bayesian Statistics
Navigating Complexities in Bayesian Statistics
This chapter explores the multifaceted applications of Bayesian statistics, particularly in military data fusion and challenges in model fitting. It discusses the critical role of priors, data properties, and proper model complexity in ensuring accurate interpretations of overlapping data. Additionally, the conversation highlights the importance of specialized knowledge in Bayesian methods and improving usability in probabilistic programming.
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
Getting Daniel Lee on the show is a real treat — with 20 years of experience in numeric computation; 10 years creating and working with Stan; 5 years working on pharma-related models, you can ask him virtually anything. And that I did…
From joint models for estimating oncology treatment efficacy to PK/PD models; from data fusion for U.S. Navy applications to baseball and football analytics, as well as common misconceptions or challenges in the Bayesian world — our conversation spans a wide range of topics that I’m sure you’ll appreciate!
Daniel studied Mathematics at MIT and Statistics at Cambridge University, and, when he’s not in front of his computer, is a savvy basketball player and… a hip hop DJ — you actually have his SoundCloud profile in the show notes if you’re curious!
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.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
- Daniel on Linkedin: https://www.linkedin.com/in/syclik/
- Daniel on Twitter: https://twitter.com/djsyclik
- Daniel on GitHub: https://github.com/syclik
- Daniel's DJ profile: https://soundcloud.com/dj-syclik
- LBS #91, Exploring European Football Analytics, with Max Göbel: https://learnbayesstats.com/episode/91-exploring-european-football-analytics-max-gobel/
- LBS #85, A Brief History of Sports Analytics, with Jim Albert: https://learnbayesstats.com/episode/85-brief-history-sports-analytics-jim-albert/
- Daniel about GPTs in Probabilistic Programming: https://www.youtube.com/watch?v=KUuSwLMFPHM
- LBS #50, Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalter/
- LBS #76, The Past, Present & Future of Stan, with Bob Carpenter: https://learnbayesstats.com/episode/76-past-present-future-of-stan-bob-carpenter/
- 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 #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/
Abstract
Our guest this week, Daniel Lee, is a real Bayesian allrounder and will give us new insights into a lot of Bayesian applications.
Daniel got introduced to Bayesian stats when trying to estimate the failure rate of satellite dishes as an undergraduate student. He was lucky to be mentored by Bayesian greats like David Spiegelhalter, Andrew Gelman and Bob Carpenter. He also sat in on reading groups at universities where he learned about cutting edge developments - something he would recommend anyone to really dive deep into the matter.
He used all this experience working on Pk/Pd (Pharmacokinetics/ Pharmacodynamics) models. We talk about the challenges in understanding individual responses to drugs based on the speed with which they move through the body. Bayesian statistics allows for incorporating more complexity into those models for more accurate estimation.
Daniel also worked on decision making and information fusing problems for the military, such as identifying a plane as friend or foe through the radar of several ships.
And to add even more diversity to his repertoire, Daniel now also works in the world of sports analytics, another popular topic on our show. We talk about the state of this emerging field and its challenges.
Finally, we cover some STAN news, discuss common problems and misconceptions around Bayesian statistics and how to resolve them.
Transcript
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