Learning Bayesian Statistics #41 Thinking Bayes, with Allen Downey
Downey's Background
- Allen Downey's background is in computer science, focusing on data measurement and modeling.
- His PhD is in computer science, and he has taught at colleges since, starting at Colby College and now at Olin College.
Transition to Statistics
- Downey transitioned to statistics through his work with data and modeling in computer science.
- He began teaching statistics at Olin College due to student dissatisfaction with the existing math-heavy statistics course.
Introduction to Bayesian Stats
- Downey's first introduction to Bayesian statistics was at a cocktail party, where he learned about eliciting priors from experts.
- Initially shocked by the subjective nature of priors, he eventually recognized their value in incorporating prior knowledge.
Let’s think Bayes, shall we? And who better to do that than the author of the well known book, Think Bayes — Allen Downey himself! Since the second edition was just released, the timing couldn’t be better!
Allen is a professor at Olin College and the author of books related to software and data science, including Think Python, Think Bayes, and Think Complexity. His blog, Probably Overthinking It, features articles on Bayesian probability and statistics. He holds a Ph.D. from U.C. Berkeley, and bachelors and masters degrees from MIT.
In this special episode, Allen and I talked about his background, how he came to the stats and teaching worlds, and why he wanted to write this book in the first place. He’ll tell us who this book is written for, what’s new in the second edition, and which mistakes his students most commonly make when starting to learn Bayesian stats. We also talked about some types of models, their usefulness and their weaknesses, but I’ll let you discover that.
Now for another good news: 5 Patrons of the show will get Think Bayes for free! To qualify, you just need to go the form I linked to in the 'Learn Bayes Stats' Slack channel or the Patreon page and enter your email address. That’s it. After a week or so, Allen and I will choose 5 winners at random, who will receive the book for free!
If you’re not a Patron yet, make sure to check out patreon.com/learnbayesstats if you don’t want to miss out on these goodies!
And even if you’re not a Patron, I love you dear listeners, so you all get a discount when you go buy the book at https://www.learnbayesstats.com/buy-think-bayes (unfortunately, this only applies for purchases in the US and Canada).
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, Jon Berezowski, 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, Jonathan Sedar, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, John Johnson and Hector Munoz.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
- Give LBS a 5-star rating on Podchaser: https://www.podchaser.com/learnbayesstats
- Buy Think Bayes at a 40% discount with the code LBS40 (expires on July 31; only applies for purchases in the US and Canada): https://www.learnbayesstats.com/buy-think-bayes
- Think Bayes 2 online: http://allendowney.github.io/ThinkBayes2/index.html
- Allen's blog: https://www.allendowney.com/blog/
- Allen on Twitter: https://twitter.com/allendowney
- Allen on GitHub: https://github.com/AllenDowney
- Information theory, inference and learning algorithms, David MacKay: https://www.inference.org.uk/itila/
- Statistical Rethinking, Richard McElreath: http://xcelab.net/rm/statistical-rethinking/
- Doing Bayesian Data Analysis, John Kruschke: https://sites.google.com/site/doingbayesiandataanalysis/home
- Probabilistic Programming & Bayesian Methods for Hackers, Cam Davidson-Pilon: http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
- LBS #14, Hidden Markov Models & Statistical Ecology, with Vianey Leos-Barajas: https://www.learnbayesstats.com/episode/14-hidden-markov-models-statistical-ecology-with-vianey-leos-barajas
- The Prosecutor's fallacy: https://en.wikipedia.org/wiki/Prosecutor%27s_fallacy
- Confidence intervals vs. Bayesian intervals, E.T. Jaynes: https://bayes.wustl.edu/etj/articles/confidence.pdf
- Superforecasting, The Art and Science of Prediction, Philip Tetlock: https://en.wikipedia.org/wiki/Superforecasting:_The_Art_and_Science_of_Prediction