#42 How to Teach and Learn Bayesian Stats, with Mine Dogucu
Learning Bayesian Statistics
Is There One Good Prior?
i think it's that m c, m c will tell you if something is really going wrong. And i tell you that your model has first. The answer is the same as for prior well, try one. If it doesn't work, you will know it. You will notice identit. Yes, differently, notes dead too.
Episode sponsored by Paperpile: paperpile.com
Get 20% off until December 31st with promo code GOODBAYESIAN21
We often talk about applying Bayesian statistics on this podcast. But how do we teach them? What’s the best way to introduce them from a young age and make sure the skills students learn in the stats class are transferable?
Well, lucky us, Mine Dogucu’s research tackles precisely those topics!
An Assistant Professor of Teaching in the Department of Statistics at University of California Irvine, Mine is both an educator with an interest in statistics, and an applied statistician with experience in educational research.
Her work focuses on modern pedagogical approaches in the statistics curriculum, making data science education more accessible. In particular, she teaches an undergraduate Bayesian course, and is the coauthor of the upcoming book Bayes Rules! An Introduction to Bayesian Modeling with R.
In other words, Mine is not only interested in teaching, but also in how best to teach statistics – how to engage students in remote classes, how to get to know them, how to best record and edit remote courses, etc. She writes about these topics on her blog, DataPedagogy.com.
She also works on accessibility and inclusion, as well as a study that investigates how popular Bayesian courses are at the undergraduate level in the US — that should be fun to talk about!
Mine did her Master’s at Bogazici University in Istanbul, Turkey, and then her PhD in Quantitative Research, Evaluation, and Measurement at Ohio State University.
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, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode and Patrick Kelley.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
- Mine's website: https://mdogucu.ics.uci.edu/index.html
- Mine's blog: https://www.datapedagogy.com/
- Mine on Twitter: https://twitter.com/MineDogucu
- Mine on GitHub: https://github.com/mdogucu
- Bayes Rules! An Introduction to Bayesian Modeling with R: https://www.bayesrulesbook.com/
- R package for Supplemental Materials for the Bayes Rules! Book: https://github.com/bayes-rules/bayesrules
- Stats 115 - Introduction to Bayesian Data Analysis: https://www.stats115.com/
- Undergraduate Bayesian Education Network: https://undergrad-bayes.netlify.app/network.html
- Workshop "Teaching Bayesian Statistics at the Undergraduate Level": https://www.causeweb.org/cause/uscots/uscots21/workshop/4
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