# #151 Diffusion Models in Python, a Live Demo with Jonas Arruda

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Podcast: [Learning Bayesian Statistics](https://stenobird.com/podcast/learning-bayesian-statistics)
Published: 2026-02-12T12:30:00+00:00
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Duration seconds: 5743

## Resource

• 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

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