Today's clip is from Episode 164, featuring Andrew Gelman, Aki Vehtari &amp; Richard McElreath. In this conversation, Andrew explains why a Bayesian workflow goes far beyond simply fitting a model. He discusses the importance of building, fitting, and checking models, and why moving between simpler and more complicated models can reveal insights that a single model might miss. He also explores how simulation and generative modeling can help researchers evaluate new models and gain confidence in their results, even when there isn't an established method or published study to rely on. It's a look at why good statistical practice isn't just about getting an answer, but knowing how much you can trust it. Full discussion <a rel="noopener noreferrer nofollow" href="https://learnbayesstats.com/episode/bayesian-workflow-reverse-bayes-hierarchical-pooling-gelman-vehtari-mcelreath" target="_blank">here</a>Support &amp; Resources → Support the show on <a rel="noopener noreferrer nofollow" href="https://www.patreon.com/c/learnbayesstats" target="_blank">Patreon</a> → <a rel="noopener noreferrer nofollow" href="https://topmate.io/alex_andorra/1011122" target="_blank">Bayesian Modeling Course</a> (first 2 lessons free): Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome <a rel="noopener noreferrer nofollow" href="https://bababrinkman.com/" target="_blank">work!</a>

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. By day, I'm a Senior data scientist. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. I also love 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 unlock exclusive Bayesian swag on Patreon!