<p>• Support &amp; <a rel="noopener noreferrer nofollow" href="https://www.patreon.com/c/learnbayesstats" target="_blank"><b>get perks</b></a>!</p><p>• Proudly sponsored by PyMC Labs! Get in touch at <a rel="noopener noreferrer nofollow" href="mailto:[email protected]" target="_blank"><b>[email protected]</b></a></p><p>• <a rel="noopener noreferrer nofollow" href="https://topmate.io/alex_andorra/503302" target="_blank"><b>Intro to Bayes</b></a> and <a rel="noopener noreferrer nofollow" href="https://topmate.io/alex_andorra/1011122" target="_blank"><b>Advanced Regression</b></a> courses (first 2 lessons free)</p><p></p><p>Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out <a rel="noopener noreferrer nofollow" href="https://bababrinkman.com/" target="_blank"><b>his awesome work </b></a>!</p><p></p><p>Chapters:<br />00:00 Exploring Generative AI and Scientific Modeling<br />10:27 Understanding Simulation-Based Inference (SBI) and Its Applications<br />15:59 Diffusion Models in Simulation-Based Inference<br />19:22 Live Coding Session: Implementing Baseflow for SBI<br />34:39 Analyzing Results and Diagnostics in Simulation-Based Inference<br />46:18 Hierarchical Models and Amortized Bayesian Inference<br />48:14 Understanding Simulation-Based Inference (SBI) and Its Importance<br />49:14 Diving into Diffusion Models: Basics and Mechanisms<br />50:38 Forward and Backward Processes in Diffusion Models<br />53:03 Learning the Score: Training Diffusion Models<br />54:57 Inference with Diffusion Models: The Reverse Process<br />57:36 Exploring Variants: Flow Matching and Consistency Models<br />01:01:43 Benchmarking Different Models for Simulation-Based Inference<br />01:06:41 Hierarchical Models and Their Applications in Inference<br />01:14:25 Intervening in the Inference Process: Adding Constraints<br />01:25:35 Summary of Key Concepts and Future Directions</p><p></p><p><b>Thank you to my </b><a rel="noopener noreferrer nofollow" href="https://learnbayesstats.com/#patrons" target="_blank"><b>Patrons </b></a><b>for making this episode possible!</b></p><p></p><p>Links from the show:<br /><br />- Come meet Alex at the <a rel="noopener noreferrer nofollow" href="https://www.fieldofplay.co.uk/" target="_blank">Field of Play Conference</a> in Manchester, UK, March 27, 2026!<br />- Jonas's Diffusion for <a rel="noopener noreferrer nofollow" href="https://bayesflow-org.github.io/diffusion-experiments/" target="_blank">SBI Tutorial</a> &amp; Review (Paper &amp; Code)<br />- The <a rel="noopener noreferrer nofollow" href="https://bayesflow.org/main/index.html#" target="_blank">BayesFlow Library</a><br />- Jonas on <a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/jonas-arruda/" target="_blank">LinkedIn</a><br />- Jonas on <a rel="noopener noreferrer nofollow" href="https://github.com/arrjon" target="_blank">GitHub</a><br />- Further reading for more mathematical details: <a rel="noopener noreferrer nofollow" href="https://arxiv.org/abs/2506.02070" target="_blank">Holderrieth &amp; Erives</a><br />- <a rel="noopener noreferrer nofollow" href="https://learnbayesstats.com/episode/150-fast-bayesian-deep-learning-with-david-rgamer-emanuel-sommer-jakob-robnik" target="_blank">150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer &amp; Jakob Robnik</a><br />- <a rel="noopener noreferrer nofollow" href="https://learnbayesstats.com/episode/107-amortized-bayesian-inference-with-deep-neural-networks-with-marvin-schmitt" target="_blank">107 Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt</a></p>