Show Notes
• Support & get perks! • Proudly sponsored by PyMC Labs! • Intro to Bayes and Advanced Regression courses (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work !
Chapters: 00:00 Exploring Generative AI and Scientific Modeling 10:27 Understanding Simulation-Based Inference (SBI) and Its Applications 15:59 Diffusion Models in Simulation-Based Inference 19:22 Live Coding Session: Implementing Baseflow for SBI 34:39 Analyzing Results and Diagnostics in Simulation-Based Inference 46:18 Hierarchical Models and Amortized Bayesian Inference 48:14 Understanding Simulation-Based Inference (SBI) and Its Importance 49:14 Diving into Diffusion Models: Basics and Mechanisms 50:38 Forward and Backward Processes in Diffusion Models 53:03 Learning the Score: Training Diffusion Models 54:57 Inference with Diffusion Models: The Reverse Process 57:36 Exploring Variants: Flow Matching and Consistency Models 01:01:43 Benchmarking Different Models for Simulation-Based Inference 01:06:41 Hierarchical Models and Their Applications in Inference 01:14:25 Intervening in the Inference Process: Adding Constraints 01:25:35 Summary of Key Concepts and Future Directions
Thank you to my Patrons for making this episode possible!
Links from the show:
- Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026! - Jonas's Diffusion for SBI Tutorial & Review (Paper & Code) - The BayesFlow Library - Jonas on LinkedIn - Jonas on GitHub - Further reading for more mathematical details: Holderrieth & Erives - 150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik - 107 Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt
More from Learning Bayesian Statistics
Support & Resources → Support the show on Patreon → Bayesian Modeling Course (first 2 lessons free)
Today's clip is from Episode 166, featuring Bill Engels & Jesse Grabowski. In this conversation, Bill explains what a kernel means in a Gaussian process and how it defines the idea of similarity between data points. He then breaks down the role of the length scale, showing how it controls how quickly the relationship between two points changes, and how amplitude controls the overall scale of the functions a GP can represent.
Support & Resources → Support the show on Patreon → Bayesian Modeling Course (first 2 lessons free)
Today's clip is from Episode 165, featuring Alex Fengler. In this conversation, Alex introduces Bayesify , a tool that uses AI to analyze research papers and assess how well they follow a Bayesian workflow.
Support & Resources → Support the show on Patreon → Bayesian Modeling Course (first 2 lessons free)