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#70 Teaching Bayes for Biology & Biological Engineering, with Justin Bois

#70 Teaching Bayes for Biology & Biological Engineering, with Justin Bois

FromLearning Bayesian Statistics


#70 Teaching Bayes for Biology & Biological Engineering, with Justin Bois

FromLearning Bayesian Statistics

ratings:
Length:
66 minutes
Released:
Oct 22, 2022
Format:
Podcast episode

Description

Proudly sponsored by https://www.pymc-labs.io/ (PyMC Labs), the Bayesian Consultancy. https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf (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/ (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 (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
Links from the show:
Justin’s website: http://bois.caltech.edu/index.html (http://bois.caltech.edu/index.html) 
Justin on GitHub: https://github.com/justinbois/ (https://github.com/justinbois/)
Justin’s course on Data analysis with frequentist inference: https://bebi103a.github.io/ (https://bebi103a.github.io/)
Justin’s course on Bayesian inference: https://bebi103b.github.io/ (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/ (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/9780815344506 (https://www.routledge.com/Physical-Biology-of-the-Cell/Phillips-Kondev-Theriot-Garcia-Phillips-Kondev-Theriot-Garcia/p/book/9780815344506)
Knowledge Illusion – Why We Never Think Alone:...
Released:
Oct 22, 2022
Format:
Podcast episode

Titles in the series (100)

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way, and I live in Paris. By day, I'm a data scientist and modeler at the https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (election forecasting) and, most importantly, Nutella. But I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)! This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy