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• Support & get perks (  / learnbayesstats  ) ! • Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com (mailto:alex.andorra@pymc-labs.com) • Intro to Bayes (https://topmate.io/alex_andorra/503302) and Advanced Regression (https://topmate.io/alex_andorra/1011122) 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 (https://bababrinkman.com/) ! Chapters: 00:00 Scaling Bayesian Neural Networks 04:26 Origin Stories of the Researchers 09:46 Research Themes in Bayesian Neural Networks 12:05 Making Bayesian Neural Networks Fast 16:19 Microcanonical Langevin Sampler Explained 22:57 Bottlenecks in Scaling Bayesian Neural Networks 29:09 Practical Tools for Bayesian Neural Networks 36:48 Trade-offs in Computational Efficiency and Posterior Fidelity 40:13 Exploring High Dimensional Gaussians 43:03 Practical Applications of Bayesian Deep Ensembles 45:20 Comparing Bayesian Neural Networks with Standard Approaches 50:03 Identifying Real-World Applications for Bayesian Methods 57:44 Future of Bayesian Deep Learning at Scale 01:05:56 The Evolution of Bayesian Inference Packages 01:10:39 Vision for the Future of Bayesian Statistics Thank you to my Patrons (https://learnbayesstats.com/#patrons) for making this episode possible! Come meet Alex at the Field of Play Conference (https://www.fieldofplay.co.uk/) in Manchester, UK, March 27, 2026! Links from the show: David Rügamer: Website (https://www.statistik.uni-muenchen.de...) Google Scholar (https://scholar.google.com/citations?...) GitHub (https://github.com/compstat-lmu) Emanuel Sommer: Website (https://emanuelsommer.github.io/my-yo...) GitHub (https://github.com/emanuelsommer) Google Scholar (https://scholar.google.com/citations?...) Jakob Robnik: Google Scholar (https://scholar.google.com/citations?...) GitHub (https://github.com/JakobRobnik) Microcanonical Langevin paper (https://www.jmlr.org/papers/volume24/...) LinkedIn (  / emanuelsommer  )