Learning Bayesian Statistics

Martin Ingram, a researcher known for his work on reliable variational inference, shares valuable insights on ADVI and DADVI. He discusses the allure and pitfalls of ADVI, emphasizing tuning challenges and convergence issues. The conversation digs into the advantages and drawbacks of mean-field variational inference and introduces the innovative linear response technique for covariance estimation. Martin also contrasts stochastic and deterministic optimization, revealing how DADVI's fixed-draw method can enhance reliability while acknowledging the trade-offs involved.

Martin Ingram, a data scientist and Bayesian researcher known for his work on DADVI and contributions to PyMC, dives into fast approximate inference methods. He discusses how DADVI enhances speed and accuracy in Bayesian inference while maintaining model flexibility. The conversation covers recovering covariance estimates using linear response and contrasts deterministic optimization with stochastic methods. Martin also shares insights on the practical performance of DADVI across different models and hints at exciting future enhancements like GPU support and exploring normalizing flows.

Ethan Smith, a high energy density physicist, shares fascinating insights on the role of Bayesian inference in extreme physics. He discusses using historical data to enhance new experiments and outlines his groundbreaking project on the plasma equation of state under extreme pressures. Ethan emphasizes the importance of managing uncertainties and shares best practices for large modeling codebases. He also advocates for making Bayesian inference more accessible through modern tools, illustrating how these techniques revolutionize data analysis in complex settings.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Ethan's research involves using lasers to compress matter to extreme conditions to study astrophysical phenomena.Bayesian inference is a key tool in analyzing complex data from high energy density experiments.The future of high energy density physics lies in developing new diagnostic technologies and increasing experimental scale.High energy density physics can provide insights into planetary science and astrophysics.Emerging technologies in diagnostics are set to revolutionize the field.Ethan's dream project involves exploring picno nuclear fusion.Chapters:14:31 Understanding High Energy Density Physics and Plasma Spectroscopy21:24 Challenges in Data Analysis and Experimentation36:11 The Role of Bayesian Inference in High Energy Density Physics47:17 Transitioning to Advanced Sampling Techniques51:35 Best Practices in Model Development55:30 Evaluating Model Performance01:02:10 The Role of High Energy Density Physics01:11:15 Innovations in Diagnostic Technologies01:22:51 Future Directions in Experimental Physics01:26:08 Advice for Aspiring ScientistsThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady,

Jordan Thibodeau, an experienced HR and product professional, shares crucial insights on thriving in an AI-driven workplace. He discusses how AI can boost productivity but emphasizes the need for expert oversight to ensure quality output. Jordan highlights the significance of deep expertise and networking for junior workers aiming to join top tech firms. He also unravels the randomness of interviews and the key traits that matter most to employers. Lastly, he recommends practical steps for anyone to become an AI thought leader in their organization.

Jordan Thibodeau, a tech operator with experience at Google and Slack, dives into the transformative role of AI in the workplace. He discusses how AI can enhance productivity but emphasizes its current limitations. Jordan shares job-seeking tips for breaking into top AI firms, the importance of mentorship, and navigating corporate culture. He also touches on M&A dynamics in the AI era, balancing speculative ventures with real productivity gains. Personal stories highlight his mission to invest in cancer research, blending tech with human impact.

Join Maurizio Filippone, a Bayesian machine learning researcher specializing in Gaussian processes, as he unpacks the magic of deep Gaussian processes. He explains how composing GPs enhances flexibility and offers insights into modeling complex data. Discover practical approximations for implementing Deep GPs in TensorFlow, and learn when to use them over traditional deep neural networks. Maurizio also shares how to map neural networks to GP-like behavior for better interpretability and uncertainty quantification. It's a fascinating dive into the future of machine learning!

Maurizio Filippone, an associate professor at KAUST and leader of the Bayesian Deep Learning Group, dives into the fascinating world of Bayesian function estimation. He explains why Gaussian Processes are still crucial for function estimation and how deep Gaussian Processes introduce flexibility for complex tasks. Maurizio discusses practical strategies like Monte Carlo Dropout for uncertainty quantification in neural networks, the trade-offs between model complexity and interpretability, and the role of Bayesian methods in modern generative models.

Christoph Bamberg, a health psychology researcher, dives into the intriguing world of Bayesian statistics and its applications in appetite regulation. He discusses how the framing of dietary claims affects cognition, revealing modest influences on performance. Christoph shares insights on the challenges of using Bayesian models, especially in small-sample studies, and emphasizes the importance of communication in health contexts. He also highlights the potential of positive framing in therapeutic settings, merging scientific research with practical implications.

In this discussion, Christoph Bamberg, a researcher in cognitive science and health psychology, explores the impact of Bayesian methods on nutrition science. He shares insights on how dietary framing can influence cognition, revealing that effects of intermittent fasting depend on context and individual rhythms. Christoph emphasizes the importance of clear definitions in research and how small effects can have significant public health implications. He also highlights the challenges of converting collaborators to Bayesian methods and offers advice for students diving into this complex field.