Allen Downey's guest appearances

Allen Downey

The Python Podcast.__init__

Big Beacon Radio

Learning Bayesian Statistics

Mark Goulston

Kerry Hannon

David Spiegelhalter

Edgar Schein

Jonathan Adler

Andrew Gelman

Dan Heath

Michael Lee

Baba Brinkman

Stephen Jones

Thomas Edison

David Wright

Dan Pink

Richard Sheridan

Michael Kennedy

OwlTail

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/learnbayesstatsBuy 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-bayesThink Bayes 2 online: http://allendowney.github.io/ThinkBayes2/index.htmlAllen's blog: https://www.allendowney.com/blog/Allen on Twitter: https://twitter.com/allendowneyAllen on GitHub: https://github.com/AllenDowneyInformation 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/homeProbabilistic Programming &amp; Bayesian Methods for Hackers, Cam Davidson-Pilon: http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/LBS #14, Hidden Markov Models &amp; Statistical Ecology, with Vianey Leos-Barajas: https://www.learnbayesstats.com/episode/14-hidden-markov-models-statistical-ecology-with-vianey-leos-barajasThe Prosecutor's fallacy: https://en.wikipedia.org/wiki/Prosecutor%27s_fallacyConfidence intervals vs. Bayesian intervals, E.T. Jaynes: https://bayes.wustl.edu/etj/articles/confidence.pdfSuperforecasting, The Art and Science of Prediction, Philip Tetlock: https://en.wikipedia.org/wiki/Superforecasting:_The_Art_and_Science_of_PredictionThis podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy

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 <a href="https://www.patreon.com/learnbayesstats" rel="noopener noreferrer nofollow" target="_blank">the Patreon page</a> 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 <a href="https://www.patreon.com/learnbayesstats" rel="noopener noreferrer nofollow" target="_blank">patreon.com/learnbayesstats</a> 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 <a href="https://www.learnbayesstats.com/buy-think-bayes" rel="noopener noreferrer nofollow" target="_blank">https://www.learnbayesstats.com/buy-think-bayes</a> (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 <a href="https://bababrinkman.com/" rel="noopener noreferrer nofollow" target="_blank">https://bababrinkman.com/</a> !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 <a href="https://www.patreon.com/learnbayesstats" rel="noopener noreferrer nofollow" target="_blank">https://www.patreon.com/learnbayesstats</a> to unlock exclusive Bayesian swag ;)Links from the show:<ul><li>Give LBS a 5-star rating on Podchaser: <a href="https://www.podchaser.com/learnbayesstats" rel="noopener noreferrer nofollow" target="_blank">https://www.podchaser.com/learnbayesstats</a></li><li>Buy Think Bayes at a 40% discount with the code LBS40 (expires on July 31; only applies for purchases in the US and Canada): <a href="https://www.learnbayesstats.com/buy-think-bayes" rel="noopener noreferrer nofollow" target="_blank">https://www.learnbayesstats.com/buy-think-bayes</a></li><li>Think Bayes 2 online: <a href="http://allendowney.github.io/ThinkBayes2/index.html" rel="noopener noreferrer nofollow" target="_blank">http://allendowney.github.io/ThinkBayes2/index.html</a></li><li>Allen's blog: <a href="https://www.allendowney.com/blog/" rel="noopener noreferrer nofollow" target="_blank">https://www.allendowney.com/blog/</a></li><li>Allen on Twitter: <a href="https://twitter.com/allendowney" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/allendowney</a></li><li>Allen on GitHub: <a href="https://github.com/AllenDowney" rel="noopener noreferrer nofollow" target="_blank">https://github.com/AllenDowney</a></li><li>Information theory, inference and learning algorithms, David MacKay: <a href="https://www.inference.org.uk/itila/" rel="noopener noreferrer nofollow" target="_blank">https://www.inference.org.uk/itila/</a></li><li>Statistical Rethinking, Richard McElreath: <a href="http://xcelab.net/rm/statistical-rethinking/" rel="noopener noreferrer nofollow" target="_blank">http://xcelab.net/rm/statistical-rethinking/</a></li><li>Doing Bayesian Data Analysis, John Kruschke: <a href="https://sites.google.com/site/doingbayesiandataanalysis/home" rel="noopener noreferrer nofollow" target="_blank">https://sites.google.com/site/doingbayesiandataanalysis/home</a></li><li>Probabilistic Programming &amp; Bayesian Methods for Hackers, Cam Davidson-Pilon: <a href="http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/" rel="noopener noreferrer nofollow" target="_blank">http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/</a></li><li>LBS #14, Hidden Markov Models &amp; Statistical Ecology, with Vianey Leos-Barajas: <a href="https://www.learnbayesstats.com/episode/14-hidden-markov-models-statistical-ecology-with-vianey-leos-barajas" rel="noopener noreferrer nofollow" target="_blank">https://www.learnbayesstats.com/episode/14-hidden-markov-models-statistical-ecology-with-vianey-leos-barajas</a></li><li>The Prosecutor's fallacy: <a href="https://en.wikipedia.org/wiki/Prosecutor%27s_fallacy" rel="noopener noreferrer nofollow" target="_blank">https://en.wikipedia.org/wiki/Prosecutor%27s_fallacy</a></li><li>Confidence intervals vs. Bayesian intervals, E.T. Jaynes: <a href="https://bayes.wustl.edu/etj/articles/confidence.pdf" rel="noopener noreferrer nofollow" target="_blank">https://bayes.wustl.edu/etj/articles/confidence.pdf</a></li><li>Superforecasting, The Art and Science of Prediction, Philip Tetlock: <a href="https://en.wikipedia.org/wiki/Superforecasting:_The_Art_and_Science_of_Prediction" rel="noopener noreferrer nofollow" target="_blank">https://en.wikipedia.org/wiki/Superforecasting:_The_Art_and_Science_of_Prediction</a></li></ul>This podcast uses the following third-party services for analysis: Podcorn - <a href="https://podcorn.com/privacy" rel="nofollow" target="_blank">https://podcorn.com/privacy</a>

#41 Thinking Bayes, with Allen Downey

In this episode, host Dave Goldberg is joined by Allen Downey, professor of Computer Science at Olin College. Through his work at Olin, Allen has been presented with unique opportunities to develop new curricular materials and influence engineering education: such as introducing modeling and simulation early in the curriculum and applying it throughout, and taking computational approaches to topics that are traditionally presented mathematically. Join Allen and Dave as they explore how modern programming language provides new tools for teaching, learning, exploring, and thinking, and delve into the importance of peer learning and visualization.

Programming as a Way of Thinking: An Interview with Allen Downey