Learning Bayesian Statistics

Alexandre Andorra

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way, and I live in Estonia. By day, I'm a data scientist and modeler at the https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (election forecasting) and, most importantly, Nutella. But I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)!

  1. Fast Approximate Inference without Convergence Worries, with Martin Ingram

    HACE 7 H

    Fast Approximate Inference without Convergence Worries, with Martin Ingram

    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: DADVI is a new approach to variational inference that aims to improve speed and accuracy.DADVI allows for faster Bayesian inference without sacrificing model flexibility.Linear response can help recover covariance estimates from mean estimates.DADVI performs well in mixed models and hierarchical structures.Normalizing flows present an interesting avenue for enhancing variational inference.DADVI can handle large datasets effectively, improving predictive performance.Future enhancements for DADVI may include GPU support and linear response integration. Chapters: 13:17 Understanding DADVI: A New Approach 21:54 Mean Field Variational Inference Explained 26:38 Linear Response and Covariance Estimation 31:21 Deterministic vs Stochastic Optimization in DADVI 35:00 Understanding DADVI and Its Optimization Landscape 37:59 Theoretical Insights and Practical Applications of DADVI 42:12 Comparative Performance of DADVI in Real Applications 45:03 Challenges and Effectiveness of DADVI in Various Models 48:51 Exploring Future Directions for Variational Inference 53:04 Final Thoughts and Advice for Practitioners Thank 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...

    1 h y 10 min
  2. Lasers, Planets, and Bayesian Inference, with Ethan Smith

    27 NOV

    Lasers, Planets, and Bayesian Inference, with Ethan Smith

    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 Spectroscopy 21:24 Challenges in Data Analysis and Experimentation 36:11 The Role of Bayesian Inference in High Energy Density Physics 47:17 Transitioning to Advanced Sampling Techniques 51:35 Best Practices in Model Development 55:30 Evaluating Model Performance 01:02:10 The Role of High Energy Density Physics 01:11:15 Innovations in Diagnostic Technologies 01:22:51 Future Directions in Experimental Physics 01:26:08 Advice for Aspiring Scientists Thank 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,

    1 h y 35 min
  3. Career Advice in the Age of AI, with Jordan Thibodeau

    12 NOV

    Career Advice in the Age of AI, with Jordan Thibodeau

    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 ;) Thank 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. Takeaways: AI is reshaping the workplace, but we're still in early stages.Networking is crucial for job applications in top firms.AI tools can augment work but are not replacements for skilled labor.Understanding the tech landscape requires continuous learning.Timing and cultural readiness are key for tech innovations.Expertise can be gained without formal education.Bayesian statistics is a valuable skill for tech professionals.The importance of personal branding in the job market. You just need to know 1% more than the person you're talking to.Sharing knowledge can elevate your status within a company.Embracing chaos in tech can create new opportunities.Investing in people leads...

    1 h y 52 min
  4. Why is Bayesian Deep Learning so Powerful, with Maurizio Filippone

    30 OCT

    Why is Bayesian Deep Learning so Powerful, with Maurizio Filippone

    Sign up for Alex's first live cohort, about Hierarchical Model building!Get 25% off "Building AI Applications for Data Scientists and Software Engineers" Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! 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: Why GPs still matter: Gaussian Processes remain a go-to for function estimation, active learning, and experimental design – especially when calibrated uncertainty is non-negotiable.Scaling GP inference: Variational methods with inducing points (as in GPflow) make GPs practical on larger datasets without throwing away principled Bayes.MCMC in practice: Clever parameterizations and gradient-based samplers tighten mixing and efficiency; use MCMC when you need gold-standard posteriors.Bayesian deep learning, pragmatically: Stochastic-gradient training and approximate posteriors bring Bayesian ideas to neural networks at scale.Uncertainty that ships: Monte Carlo dropout and related tricks provide fast, usable uncertainty – even if they’re approximations.Model complexity ≠ model quality: Understanding capacity, priors, and inductive bias is key to getting trustworthy predictions.Deep Gaussian Processes: Layered GPs offer flexibility for complex functions, with clear trade-offs in interpretability and compute.Generative models through a Bayesian lens: GANs and friends benefit from explicit priors and uncertainty – useful for safety and downstream decisions.Tooling that matters: Frameworks like GPflow lower the friction from idea to implementation, encouraging reproducible, well-tested modeling.Where we’re headed: The future of ML is uncertainty-aware by default – integrating UQ tightly into optimization, design, and deployment. Chapters: 08:44 Function Estimation and Bayesian Deep Learning 10:41 Understanding Deep Gaussian Processes 25:17 Choosing Between Deep GPs and Neural Networks 32:01 Interpretability and Practical Tools for GPs 43:52 Variational Methods in Gaussian Processes 54:44 Deep Neural Networks and Bayesian Inference 01:06:13 The Future of Bayesian Deep Learning 01:12:28 Advice for Aspiring Researchers p...

    1 h y 28 min

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Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way, and I live in Estonia. By day, I'm a data scientist and modeler at the https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (election forecasting) and, most importantly, Nutella. But I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)!

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