Why a Bayesian Workflow Goes Beyond Fitting Models

<p>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. <br /><br />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. <br /><br />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. <br /></p><p>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></p><p></p><p>Support &amp; Resources<br />→ Support the show on <a rel="noopener noreferrer nofollow" href="https://www.patreon.com/c/learnbayesstats" target="_blank">Patreon</a><br />→ <a rel="noopener noreferrer nofollow" href="https://topmate.io/alex_andorra/1011122" target="_blank">Bayesian Modeling Course</a> (first 2 lessons free): <br />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></p>