#53 Bayesian Stats for the Behavioral & Neural Sciences, with Todd Hudson
Learning Bayesian Statistics
Is There a Mistake in the Model Comparison?
The author of a book on computer science says he has made mistakes in his own work. He explains how to fix them, and why it's important to have a simulation stage for experiments. The best way to catch errors is by running simulations through the equations you've written.
Get a 30% discount on Todd's book by entering the code BDABNS22 at checkout!
The behavioral and neural sciences are a nerdy interest of mine, but I didn’t dedicate any episode to that topic yet. But life brings you gifts sometimes (especially around Christmas…), and here that gift is a book, Bayesian Data Analysis for the Behavioral and Neural Sciences, by Todd Hudson.
Todd is a part of the faculty at New York University Grossman School of Medicine and also the New York University Tandon School of Engineering. He is a computational neuroscientist working in several areas including: early detection and grading of neurological disease; computational models of movement planning and learning; development of new computational and experimental techniques.
He also co-founded Tactile Navigation Tools, which develops navigation aids for the visually impaired, and Third Eye Technologies, which develops low cost laboratory- and clinical-grade eyetracking technologies.
As you’ll hear, Todd wanted his book to bypass the need for advanced mathematics normally considered a prerequisite for this type of material. Basically, he wants students to be able to write code and models and understand equations, even they are not specialized in writing those equations.
We’ll also touch on some of the neural sciences examples he’s got in the book, as well as the two general algorithms he uses for model measurement and model selection.
Ow, I almost forgot the most important: Todd loves beekeeping and gardening — he’s got 25 apple trees, 4 cherry trees, nectarines, figs, strawberries, etc!
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, 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, Alejandro Morales, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones and Daniel Lindroth.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
- 30% discount on Todd's book by entering BDABNS22 at checkout: https://www.cambridge.org/it/academic/subjects/psychology/psychology-general-interest/bayesian-data-analysis-behavioral-and-neural-sciences-non-calculus-fundamentals?format=PB&isbn=9781108812900
- Book's webpage: https://www.hudsonlab.org/textbook
- For blurbs on each chapter: https://www.hudsonlab.org/textbookresources
- Code used in each chapter: https://www.hudsonlab.org/textbook/f314a
- For tutorials on Bayesian vs. classical frequentist data analysis: https://www.hudsonlab.org/datatutorials
- Todd's research: https://www.hudsonlab.org/research
- Todd's webpage: https://med.nyu.edu/faculty/todd-e-hudson
- For information on Todd's assistive technology work: feeltnt.com
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