<p>Today’s clip is from <a href="https://learnbayesstats.com/episode/147-fast-approximate-inference-without-convergence-worries-martin-ingram" rel="noopener noreferrer" target="_blank">episode 147</a> of the podcast, with Martin Ingram.</p><p>Alex and Martin discuss the intricacies of variational inference, particularly focusing on the ADVI method and its challenges. They explore the evolution of approximate inference methods, the significance of mean field variational inference, and the innovative linear response technique for covariance estimation. </p><p>The discussion also delves into the trade-offs between stochastic and deterministic optimization techniques, providing insights into their implications for Bayesian statistics.</p><p>Get the full discussion <a href="https://learnbayesstats.com/episode/147-fast-approximate-inference-without-convergence-worries-martin-ingram" rel="noopener noreferrer" target="_blank">here</a>.</p><ul><li><a href="https://topmate.io/alex_andorra/503302" rel="noopener noreferrer" target="_blank">Intro to Bayes Course</a> (first 2 lessons free)</li><li><a href="https://topmate.io/alex_andorra/1011122" rel="noopener noreferrer" target="_blank">Advanced Regression Course</a> (first 2 lessons free)</li></ul><br/><p><em>Our&nbsp;theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and&nbsp;Mega Ran). Check out his </em><a href="https://bababrinkman.com/" rel="noopener noreferrer" target="_blank"><em>awesome work</em></a><em>!</em></p><p>Visit our <a href="https://www.patreon.com/learnbayesstats" rel="noopener noreferrer" target="_blank">Patreon page</a> to unlock exclusive Bayesian swag ;)</p><p><strong>Transcript</strong></p><p class="ql-align-justify"><em>This is an automatic transcript and may therefore contain errors. Please </em><a href="http://twitter.com/LearnBayesStats" rel="noopener noreferrer" target="_blank"><em>get in touch</em></a><em> if you're willing to correct them.</em></p>