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 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:

• Martin's website: https://martiningram.github.io/
• Martin on Linkedin: https://www.linkedin.com/in/martin-ingram-48302782/
• Martin on GitHub: https://github.com/martiningram 
• Martin on Google Scholar: https://scholar.google.com/citations?user=AZ-A7AEAAAAJ&hl=en
• Fast approximate inference without convergence worries in PyMC: https://martiningram.github.io/deterministic-advi-in-pymc/
• DADVI linear regression example: https://github.com/pymc-devs/pymc-extras/blob/main/notebooks/deterministic_advi_example.ipynb
• LBS #142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte: https://learnbayesstats.com/episode/142-bayesian-trees-deep-learning-optimization-big-data-gabriel-stechschulte
• Alex Andorra & Chris Fonnesbeck – A Beginner's Guide to Variational Inference | PyData Virginia 2025: https://www.youtube.com/watch?v=XECLmgnS6Ng
• NUTS Adaptation with Normalizing Flows: https://pymc-devs.github.io/nutpie/nf-adapt.html

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Today’s clip is from episode 146 of the podcast, with Ethan Smith.

Alex and Ethan discuss the application of Bayesian inference in high energy density physics, particularly in analyzing complex data sets. They highlight the advantages of Bayesian techniques, such as incorporating prior knowledge and managing uncertainties.

They also shares insights from an ongoing experimental project focused on measuring the equation of state of plasma at extreme pressures. Finally, Alex and Ethan advocate for best practices in managing large codebases and ensuring model reliability.

Get the full discussion here.

• 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 ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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, 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=en
• LBS #47 Bayes in Physics & Astrophysics, with JJ Ruby: https://learnbayesstats.com/episode/47-bayes-physics-astrophysics-jj-ruby
• Principled Bayesian Workflow: https://arxiv.org/abs/2011.01808
• CMAP video from Vox: https://www.youtube.com/watch?v=NqabT21d8VM
• Atlantic 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

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Today’s clip is from episode 145 of the podcast, with Jordan Thibodeau.

Alexandre Andorra and Jordan Thibodeau discuss the transformative impact of AI on productivity, career opportunities in the tech industry, and the intricacies of the job interview process.

They emphasize the importance of expertise, networking, and the evolving landscape of tech companies, while also providing actionable advice for individuals looking to enhance their careers in AI and related fields.

Get the full discussion here.

• 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 ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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 to a more engaged workforce.
• Navigating corporate culture requires understanding your role and relationships.
• M&A trends in AI reflect the evolving landscape of technology.
• High compensation packages are not a new phenomenon in tech.
• Career growth often requires stepping outside your comfort zone.
• Soft skills are essential for effective communication in the workplace.
• Understanding the dynamics of M&A can provide insights into industry trends. AI is creating real economic value in customer service.
• Speculative activity often overshadows real economic activity in tech.
• Memorable M&A experiences can have a profound impact on people's lives.

Chapters:

10:39 The Impact of AI on Work and Culture

18:11 Understanding the AI Revolution

30:05 Career Advice in the Age of AI

38:08 Innovative Company Culture and Experimentation

41:04 Interview Dynamics and Performance Bias

42:29 Augmenting Work with AI and Learning

46:46 Navigating Organizational Boundaries

51:33 The Importance of Soft Skills in Tech

01:01:21 Mergers and Acquisitions in the Tech Industry

01:15:08 The Reality of Tech Salaries

01:18:18 The Impact of AI on Customer Service

01:20:36 Speculative vs. Real Economic Activity in Tech

01:22:33 The Cycle of Tech Booms and Busts

01:24:22 Heartfelt Stories from the M&A World

01:31:36 A Personal Vendetta Against Cancer

Links from the show:

• Jordan on YouTube: https://www.youtube.com/@SVICPodcast
• Jordan on LinkedIn: https://www.linkedin.com/in/jwthib/
• Jordan on Twitter: https://twitter.com/jordansvic
• LBS #124 State Space Models & Structural Time Series, with Jesse Grabowski: https://learnbayesstats.com/episode/124-state-space-models-structural-time-series-jesse-grabowski
• Using AI Tools to Automate Busywork and Scale Productivity | Sully Omar: https://www.youtube.com/watch?v=sk9YkYOZH8Y
• Supercommunicators – How to Unlock the Secret Language of Connection: https://www.amazon.com/Supercommunicators-Unlock-Secret-Language-Connection/dp/0593243919
• The Charisma Myth – How Anyone Can Master the Art and Science of Personal Magnetism: https://www.amazon.com/Charisma-Myth-Science-Personal-Magnetism/dp/1591845947
• The 48 Laws of Power: https://www.amazon.com/48-Laws-Power-Robert-Greene/dp/0140280197/
• There Will Be Blood: https://www.imdb.com/title/tt0469494/
• How to Invent Everything – A Survival Guide for the Stranded Time Traveler: https://www.amazon.com/How-Invent-Everything-Survival-Stranded/dp/073522014X

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Today’s clip is from episode 144 of the podcast, with Maurizio Filippone.

In this conversation, Alex and Maurizio delve into the intricacies of Gaussian processes and their deep learning counterparts. They explain the foundational concepts of Gaussian processes, the transition to deep Gaussian processes, and the advantages they offer in modeling complex data.

The discussion also touches on practical applications, model selection, and the evolving landscape of machine learning, particularly in relation to transfer learning and the integration of deep learning techniques with Gaussian processes.

Get the full discussion here.

• 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 ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

• 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

01:22:09 Tackling Global Issues with AI

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, 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, Philippe Labonde, 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, Jeannine Sue, 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, Bart Trudeau, 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 Adam Tilmar Jakobsen.

Links from the show:

• Maurizio's website: https://mauriziofilippone.github.io
• Maurizio on Google Scholar: https://scholar.google.com/citations?user=ILUeAloAAAAJ&hl=en
• GANs Secretly Perform Approximate Bayesian Model Selection: https://www.youtube.com/watch?v=pnfQ2_6jGl4 
• Videos of a couple of presentations on Bayesian Deep Learning: 
• Aalto University 2023: https://www.youtube.com/watch?v=R2T3Z-Y3LXM
• AI Center in Prague 2023: https://www.youtube.com/watch?v=xE7TaQeLAXE 
• UC Irvine 2022: https://www.youtube.com/watch?v=oZAuh686ipw 
• Video of the discussion of the paper “Deep Gaussian Processes for Calibration of Computer Models” published in Bayesian Analysis as a discussion paper: https://www.youtube.com/watch?v=K_hPbvoo0_M 
• Lecture on Deep Gaussian Processes at DeepBayes 2019: https://www.youtube.com/watch?v=750fRY9-uq8
• Lecture on Gaussian Processes at Deep Bayes 2018: https://www.youtube.com/watch?v=zBEV5ezyYmI
• A tutorial on GPs with E. V. Bonilla at IJCAI in 2021: https://ebonilla.github.io/gaussianprocesses/
• PyData Tutorial, Mastering Gaussian Processes with PyMC: https://github.com/AlexAndorra/advanced-gp-pydata#
• LBS #136 Bayesian Inference at Scale: Unveiling INLA, with Haavard Rue & Janet van Niekerk: https://learnbayesstats.com/episode/136-bayesian-inference-at-scale-unveiling-inla-haavard-rue-janet-van-niekerk
• LBS #129 Bayesian Deep Learning & AI for Science with Vincent Fortuin: https://learnbayesstats.com/episode/129-bayesian-deep-learning-ai-for-science-vincent-fortuin
• LBS #107 Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt: https://learnbayesstats.com/episode/107-amortized-bayesian-inference-deep-neural-networks-marvin-schmitt
• GPFlow documentation: https://www.gpflow.org/
• PyTorch docs: https://pytorch.org/
• Pyro docs: https://pyro.ai/

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

• Sign up for Alex's first live cohort, about Hierarchical Model building
• Soccer Factor Model Dashboard

Today’s clip is from episode 143 of the podcast, with Christoph Bamberg.

Christoph shares his journey into Bayesian statistics and computational modeling, the challenges faced in academia, and the technical tools used in research.

Alex and Christoph delve into a specific study on appetite regulation and cognitive performance, exploring the implications of framing in psychological research and the importance of careful communication in health-related contexts.

Get the full discussion here.

• 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 ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

• Sign up for Alex's first live cohort, about Hierarchical Model building!

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:

• Bayesian mindset in psychology: Why priors, model checking, and full uncertainty reporting make findings more honest and useful.
• Intermittent fasting & cognition: A Bayesian meta-analysis suggests effects are context- and age-dependent – and often small but meaningful.
• Framing matters: The way we frame dietary advice (focus, flexibility, timing) can shape adherence and perceived cognitive benefits.
• From cravings to choices: Appetite, craving, stress, and mood interact to influence eating and cognitive performance throughout the day.
• Define before you measure: Clear definitions (and DAGs to encode assumptions) reduce ambiguity and guide better study design.
• DAGs for causal thinking: Directed acyclic graphs help separate hypotheses from data pipelines and make causal claims auditable.
• Small effects, big implications: Well-estimated “small” effects can scale to public-health relevance when decisions repeat daily.
• Teaching by modeling: Helping students write models (not just run them) builds statistical thinking and scientific literacy.
• Bridging lab and life: Balancing careful experiments with real-world measurement is key to actionable health-psychology insights.
• Trust through transparency: Openly communicating assumptions, uncertainty, and limitations strengthens scientific credibility.

Chapters:

10:35 The Struggles of Bayesian Statistics in Psychology

22:30 Exploring Appetite and Cognitive Performance

29:45 Research Methodology and Causal Inference

36:36 Understanding Cravings and Definitions

39:02 Intermittent Fasting and Cognitive Performance

42:57 Practical Recommendations for Intermittent Fasting

49:40 Balancing Experimental Psychology and Statistical Modeling

55:00 Pressing Questions in Health Psychology

01:04:50 Future Directions in Research

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, 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, Philippe Labonde, 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, Jeannine Sue, 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, Bart Trudeau, 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 Adam Tilmar Jakobsen.

Links from the show:

• Sign up for Alex's first live cohort, about Hierarchical Model building: https://athlyticz.com/cohorts/alex-andorra/hierarchical
• Christoph’s website: https://christophbg.github.io/
• Christoph on LinkedIn: https://www.linkedin.com/in/christoph-bamberg-2249a9a9/
• The impact of dietary claims on behaviour – Expectations qualify how actual satiety affects cognitive performance: https://www.sciencedirect.com/science/article/pii/S0195666324006275
• Bayesian meta analysis on the effects of intermittent fasting on cognitive performance: https://doi.org/10.31234/osf.io/tgwep_v1
• randomised controlled trial on intermittent fasting, cognitive performance and mood: https://doi.org/10.1177/13591053251351204
• LBS #35 The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner
• LBS #112 Advanced Bayesian Regression, with Tomi Capretto: https://learnbayesstats.com/episode/112-advanced-bayesian-regression-tomi-capretto
• LBS #89 Unlocking the Science of Exercise, Nutrition & Weight Management, with Eric Trexler: https://learnbayesstats.com/episode/89-unlocking-science-exercise-nutrition-weight-management-eric-trexler
• LBS #137 Causal AI & Generative Models, with Robert Ness: https://learnbayesstats.com/episode/137-causal-ai-generative-models-robert-ness
• Decentralised Construct Taxonomy: https://psycore.one/ and an article about it:         https://doi.org/10.15626/MP.2022.3638

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

• Soccer Factor Model Dashboard
• Unveiling True Talent: The Soccer Factor Model for Skill Evaluation
• LBS #91, Exploring European Football Analytics, with Max Göbel

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Today’s clip is from episode 142 of the podcast, with Gabriel Stechschulte.

Alex and Garbriel explore the re-implementation of BART (Bayesian Additive Regression Trees) in Rust, detailing the technical challenges and performance improvements achieved.

They also share insights into the benefits of BART, such as uncertainty quantification, and its application in various data-intensive fields.

Get the full discussion here.

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Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.