Learning Bayesian Statistics

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, Kurt TeKolste, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Joshua Meehl, Javier Sabio, Kristian Higgins, Matt Rosinski, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık, Suyog Chandramouli and Guillaume Berthon.Links from the show:Ethan on LinkedIn: www.linkedin.com/in/ethan-smith-2903652b0 Ethan on Google Scholar: https://scholar.google.com/citations?user=nawbtW0AAAAJ&hl=enLBS #47 Bayes in Physics & Astrophysics, with JJ Ruby: https://learnbayesstats.com/episode/47-bayes-physics-astrophysics-jj-rubyPrincipled Bayesian Workflow: https://arxiv.org/abs/2011.01808CMAP video from Vox: https://www.youtube.com/watch?v=NqabT21d8VMAtlantic article about using giant lasers to study planets: https://www.theatlantic.com/science/archive/2023/01/astrophysics-fusion-experimentation-supernovas/672625/ Atomic physics in implosion plasmas: https://doi.org/10.1038/s41467-022-34618-6 X-ray diffraction and new states of matter: https://doi.org/10.1038/s41467-022-29813-4 What does this data actually look like?: https://doi.org/10.1063/5.0286001 JJ Ruby’s original Bayesian inference paper: https://doi.org/10.1103/PhysRevE.102.053210 Review article highlighting Bayesian inference in HED physics: https://doi.org/10.1063/5.0128661 Predictive modeling of implosions using surrogate models: https://doi.org/10.1063/5.0215962 Modeling equations of state as thermodynamically constrained GPs: https://doi.org/10.1063/5.0165298 Pycnonuclear fusion reactions: https://w3.pppl.gov/~fisch/fischpapers/Son_Chain_react.pdf TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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.

Gabriel Stechschulte, a Bayesian software developer known for his work with PyMCBART, dives into the re-implementation of Bayesian Additive Regression Trees (BART) in Rust. He discusses the technical hurdles and enhanced performance achieved through this project. Gabriel explains the value of BART in uncertainty quantification and how it contrasts with other tree-based methods. The conversation also covers practical aspects, like integrating BART with Python and balancing open-source contributions with a full-time job, all while exploring the innovative features of PyMCBART.

Gabriel Stechschulte is a software engineer specializing in Bayesian methods and optimization. He discusses the power of Bayesian Additive Regression Trees (BART) for uncertainty quantification and its re-implementation in Rust, enhancing performance for big data. Gabriel explores how BART contrasts with other models, its strengths in avoiding overfitting, and its integration into optimization frameworks for decision-making. He also emphasizes the importance of open-source communities, encouraging newcomers to contribute actively.