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#150

• Join this channel to get access to perks:   / learnbayesstats   • Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com • Intro to Bayes Course (first 2 lessons free): https://topmate.io/alex_andorra/503302 • Advanced Regression Course (first 2 lessons free): https://topmate.io/alex_andorra/1011122 Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ ! Takeaways: Bayesian neural networks are crucial for uncertainty quantification. Scaling Bayesian methods to high dimensions is a significant challenge. JAX offers substantial speed improvements for Bayesian sampling. Initialization errors can hinder the performance of Bayesian neural networks. Microcanonical Langevin sampler enhances sampling efficiency. Practical tools are essential for wider adoption of Bayesian methods. Understanding neural networks requires better uncertainty quantification. Ensemble methods can improve the performance of Bayesian models. Computational efficiency must be balanced with posterior fidelity. Community-driven tools are vital for advancing Bayesian deep learning. Bayesian deep ensembles provide a more flexible approximation. Sampling methods can yield better predictive performance. Uncertainty quantification is crucial for practical applications. The overhead of Bayesian methods is decreasing. Bayesian neural networks outperform standard approaches in many cases. Exploration-exploitation trade-offs are important in sampling. Future advancements may allow for Bayesian deep learning at scale. Community efforts are needed to improve Bayesian inference packages. Practical applications of Bayesian methods are expanding. Understanding life and probabilistic modeling are key future goals. 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 in Manchester, UK, March 27, 2026! https://www.fieldofplay.co.uk/ 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   General references: JAX: https://github.com/google/jax BlackJAX: https://github.com/blackjax-devs/blac... sklearn-contrib-bde: https://github.com/scikit-learn-contr... (easy to use and fast MILE for tabular data) A Beginner's guide to Variational Inference:    • Chris Fonnesbeck - A Beginner's Guide to V...   posteriors (pytorch+sampling): https://github.com/normal-computing/p... LBS #142, Bayesian Trees & Deep Learning for Optimization: https://learnbayesstats.com/episode/1... MILE paper: https://arxiv.org/abs/2502.06335