• Support & get perks [https://www.patreon.com/c/learnbayesstats]!

• Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com

• Intro to Bayes [https://topmate.io/alex_andorra/503302] and Advanced Regression [https://topmate.io/alex_andorra/1011122] courses (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work [https://bababrinkman.com/]!

Chapters:

00:00 The Importance of Decision-Making in Data Science

06:41 From Philosophy to Bayesian Statistics

14:57 The Role of Soft Skills in Data Science

18:19 Understanding Decision Theory Workflows

22:43 Shifting Focus from Accuracy to Business Value

26:23 Leveraging PyTensor for Optimization

34:27 Applying Optimal Decision-Making in Industry

40:06 Understanding Utility Functions in Regulation

41:35 Introduction to Obeisance Decision Theory Workflow

42:33 Exploring Price Elasticity and Demand

45:54 Optimizing Profit through Bayesian Models

51:12 Risk Aversion and Utility Functions

57:18 Advanced Risk Management Techniques

01:01:08 Practical Applications of Bayesian Decision-Making

01:06:54 Future Directions in Bayesian Inference

01:10:16 The Quest for Better Inference Algorithms

01:15:01 Dinner with a Polymath: Herbert Simon

Thank you to my Patrons [https://learnbayesstats.com/#patrons]for making this episode possible!

Links from the show:
 * Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026! https://www.fieldofplay.co.uk/

 * A Bayesian decision theory workflow [https://daniel-saunders-phil.github.io/imagination_machine/posts/a-bayesian-decision-theory-workflow/]
 * Daniel's website, [https://daniel-saunders-phil.github.io/imagination_machine/]LinkedIn and [https://www.linkedin.com/in/dr-daniel-saunders-97239b174/] GitHub [https://github.com/daniel-saunders-phil]
 * LBS #124 State Space Models & Structural Time Series, with Jesse Grabowski [https://learnbayesstats.com/episode/124-state-space-models-structural-time-series-with-jesse-grabowski]
 * LBS #123 BART & The Future of Bayesian Tools, with Osvaldo Martin [https://learnbayesstats.com/episode/123-bart-the-future-of-bayesian-tools-with-osvaldo-martin]
 * LBS #74 Optimizing NUTS and Developing the ZeroSumNormal Distribution, with Adrian Seyboldt [https://learnbayesstats.com/episode/74-optimizing-nuts-and-developing-the-zerosumnormal-distribution-with-adrian-seyboldt]
 * LBS #76 The Past, Present & Future of Stan, with Bob Carpenter [https://learnbayesstats.com/episode/76-the-past-present-future-of-stan-with-bob-carpenter]

#152 A Bayesian decision theory workflow, with Daniel Saunders

Today's clip is from Episode 151 [https://learnbayesstats.com/episode/151-diffusion-models-in-python-a-live-demo-with-jonas-arruda] of the podcast, with Jonas Arruda

In this conversation, Jonas Arruda explains how diffusion models generate data by learning to reverse a noise process. The idea is to start from a simple distribution like Gaussian noise and gradually remove noise until the target distribution emerges. This is done through a forward process that adds noise to clean parameters and a backward process that learns how to undo that corruption. A noise schedule controls how much noise is added or removed at each step, guiding the transformation from pure randomness back to meaningful structure.

Get the full discussion here [https://learnbayesstats.com/episode/151-diffusion-models-in-python-a-live-demo-with-jonas-arruda]

• Join this channel to get access to perks:
https://www.patreon.com/c/learnbayesstats

• 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/ !

BITESIZE | How Do Diffusion Models Work?

• Support & get perks [https://www.patreon.com/c/learnbayesstats]!

• Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com

• Intro to Bayes [https://topmate.io/alex_andorra/503302] and Advanced Regression [https://topmate.io/alex_andorra/1011122] courses (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work [https://bababrinkman.com/]!

Chapters:
00:00 Exploring Generative AI and Scientific Modeling
10:27 Understanding Simulation-Based Inference (SBI) and Its Applications
15:59 Diffusion Models in Simulation-Based Inference
19:22 Live Coding Session: Implementing Baseflow for SBI
34:39 Analyzing Results and Diagnostics in Simulation-Based Inference
46:18 Hierarchical Models and Amortized Bayesian Inference
48:14 Understanding Simulation-Based Inference (SBI) and Its Importance
49:14 Diving into Diffusion Models: Basics and Mechanisms
50:38 Forward and Backward Processes in Diffusion Models
53:03 Learning the Score: Training Diffusion Models
54:57 Inference with Diffusion Models: The Reverse Process
57:36 Exploring Variants: Flow Matching and Consistency Models
01:01:43 Benchmarking Different Models for Simulation-Based Inference
01:06:41 Hierarchical Models and Their Applications in Inference
01:14:25 Intervening in the Inference Process: Adding Constraints
01:25:35 Summary of Key Concepts and Future Directions

Thank you to my Patrons [https://learnbayesstats.com/#patrons]for making this episode possible!

Links from the show:

- Come meet Alex at the Field of Play Conference [https://www.fieldofplay.co.uk/] in Manchester, UK, March 27, 2026!
- Jonas's Diffusion for SBI Tutorial [https://bayesflow-org.github.io/diffusion-experiments/] & Review (Paper & Code)
- The BayesFlow Library [https://bayesflow.org/main/index.html#]
- Jonas on LinkedIn [https://www.linkedin.com/in/jonas-arruda/]
- Jonas on GitHub [https://github.com/arrjon]
- Further reading for more mathematical details: Holderrieth & Erives [https://arxiv.org/abs/2506.02070]
- 150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik [https://learnbayesstats.com/episode/150-fast-bayesian-deep-learning-with-david-rgamer-emanuel-sommer-jakob-robnik]
- 107 Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt [https://learnbayesstats.com/episode/107-amortized-bayesian-inference-with-deep-neural-networks-with-marvin-schmitt]

151 Diffusion Models in Python, a Live Demo with Jonas Arruda

• Support & get perks [https://www.patreon.com/c/learnbayesstats]!

• Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com

• Intro to Bayes [https://topmate.io/alex_andorra/503302] and Advanced Regression [https://topmate.io/alex_andorra/1011122] courses (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work [https://bababrinkman.com/]!


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 [https://www.fieldofplay.co.uk/] in Manchester, UK, March 27, 2026!

Links from the show:


David Rügamer:
* Website [https://www.statistik.uni-muenchen.de/people/professors/rügamer/index.html]
* Google Scholar [https://scholar.google.com/citations?user=y1p8VhsAAAAJ&hl=en]
* GitHub [https://github.com/compstat-lmu]

Emanuel Sommer:
* Website [https://emanuelsommer.github.io/my-yourney/]
* GitHub [https://github.com/emanuelsommer]
* Google Scholar [https://scholar.google.com/citations?user=qa2P1tYAAAAJ&hl=en]

Jakob Robnik:
* Google Scholar [https://scholar.google.com/citations?user=J9E2DxAAAAAJ&hl=en]
* GitHub [https://github.com/JakobRobnik]
* Microcanonical Langevin paper [https://www.jmlr.org/papers/volume24/22-1450/22-1450.pdf]
* LinkedIn [https://www.linkedin.com/in/emanuelsommer/]

#150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik

Today’s clip is from episode 149 [https://learnbayesstats.com/episode/149-the-future-of-work-in-tech-with-alana-karen] of the podcast, with Alana Karen.

This conversation explores the evolving landscape of technology, particularly in Silicon Valley, focusing on the cultural shifts due to mass layoffs, the debate over remote work, and the impact of AI on job roles and priorities. The discussion highlights the importance of adapting to these changes and preparing for the future by developing complex skills that AI cannot easily replicate.

Get the full discussion here! [https://learnbayesstats.com/episode/149-the-future-of-work-in-tech-with-alana-karen]

• Join this channel to get access to perks:
https://www.patreon.com/c/learnbayesstats

• 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/ !

BITESIZE | Building Resilience in Modern Tech Careers

• Support & get perks [https://www.patreon.com/c/learnbayesstats]!

• Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com

• Intro to Bayes [https://topmate.io/alex_andorra/503302] and Advanced Regression [https://topmate.io/alex_andorra/1011122] courses (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work [https://bababrinkman.com/]!

Chapters:

11:37 The Hard Tech Era
21:08 The Shift in Tech Work Culture
28:49 AI's Impact on Job Security and Work Dynamics
34:33 Adapting to AI: Skills for the Future
45:56 Understanding AI Models and Their Limitations
47:25 The Importance of Diversity in AI Development
54:34 Positioning Technical Talent for Job Security
57:58 Building Resilience in Uncertain Times
01:06:33 Recognizing Diverse Ambitions in Career Progression
01:12:51 The Role of Managers in Employee Retention
01:26:55 Solving Complex Problems with AI and Innovation

Thank you to my Patrons [https://learnbayesstats.com/#patrons] for making this episode possible!

Links from the show:

 * Alana's latest book [https://www.alanakaren.com/books] (Use code BAYESIAN for 10% off + a free interview preparation download PDF)
 * Alana’s Substack [https://alanakaren.substack.com]
 * Alana on Linkedin [https://www.linkedin.com/in/alanakaren/]
 * Alana on Instagram [https://www.instagram.com/alanakaren/#]
 * The Obstacle Is the Way [https://www.amazon.com/Obstacle-Way-Timeless-Turning-Triumph/dp/1591846358/ref=sr_1_1] – The Timeless Art of Turning Trials into Triumph
 * Courage Is Calling [https://www.amazon.com/Courage-Is-Calling/dp/1788166280/ref=tmm_pap_swatch_0] – Fortune Favours the Brave

#149 The Future of Work in Tech, with Alana Karen

Today’s clip is from episode 148 [https://learnbayesstats.com/episode/148-adaptive-trials-bayesian-thinking-and-learning-from-data-with-scott-berry] of the podcast, with Scott Berry.

In this conversation, Alex and Scott discuss emphasizing the shift from frequentist to Bayesian approaches in clinical trials.

They highlight the limitations of traditional trial designs and the advantages of adaptive and platform trials, particularly in the context of COVID-19 treatment.

The discussion provides insights into the complexities of trial design and the innovative methodologies that are shaping the future of medical research.

Get the full discussion here [https://learnbayesstats.com/episode/148-adaptive-trials-bayesian-thinking-and-learning-from-data-with-scott-berry]!

• Join this channel to get access to perks: https://www.patreon.com/c/learnbayesstats

• 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/ !

BITESIZE | The Trial Design That Learns in Real Time

• Support & get perks [https://www.patreon.com/c/learnbayesstats]!

• Proudly sponsored by PyMC Labs. Get in touch [https://www.pymc-labs.com/] and tell them you come from LBS!

• Intro to Bayes [https://topmate.io/alex_andorra/503302] and Advanced Regression [https://topmate.io/alex_andorra/1011122] courses (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work [https://bababrinkman.com/]!

Chapters:

13:16 Understanding Adaptive and Platform Trials

25:25 Real-World Applications and Innovations in Trials

34:11 Challenges in Implementing Bayesian Adaptive Trials

42:09 The Birth of a Simulation Tool

44:10 The Importance of Simulated Data

48:36 Lessons from High-Stakes Trials

52:53 Navigating Adaptive Trial Designs

56:55 Communicating Complexity to Stakeholders

01:02:29 The Future of Clinical Trials

01:10:24 Skills for the Next Generation of Statisticians

Thank you to my Patrons [https://www.patreon.com/c/learnbayesstats] for making this episode possible!

Yusuke Saito, Avi Bryant, Giuliano Cruz, 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, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık, Suyog Chandramouli, Guillaume Berthon, Avenicio Baca, Spencer Boucher, Krzysztof Lechowski, Danimal, Jácint Juhász, Sander and Philippe.

Links from the show:

 * Berry Consultants [https://www.berryconsultants.com/]
 * Scott's podcast [https://www.berryconsultants.com/podcast]
 * LBS #45 [https://learnbayesstats.com/episode/45-biostats-clinical-trial-design-frank-harrell] Biostats & Clinical Trial Design, with Frank Harrell

#148 Adaptive Trials, Bayesian Thinking, and Learning from Data, with Scott Berry

Today’s clip is from episode 147 [https://learnbayesstats.com/episode/147-fast-approximate-inference-without-convergence-worries-martin-ingram] of the podcast, with Martin Ingram.

Alex and Martin discuss the intricacies of variational inference, particularly focusing on the ADVI method and its challenges. They explore the evolution of approximate inference methods, the significance of mean field variational inference, and the innovative linear response technique for covariance estimation.

The discussion also delves into the trade-offs between stochastic and deterministic optimization techniques, providing insights into their implications for Bayesian statistics.

Get the full discussion here [https://learnbayesstats.com/episode/147-fast-approximate-inference-without-convergence-worries-martin-ingram].

 * Intro to Bayes Course [https://topmate.io/alex_andorra/503302] (first 2 lessons free)
 * Advanced Regression Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free)


Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work [https://bababrinkman.com/]!

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch [http://twitter.com/LearnBayesStats] if you're willing to correct them.

BITESIZE | Making Variational Inference Reliable: From ADVI to DADVI

Proudly sponsored by PyMC Labs [https://www.pymc-labs.io/], the Bayesian Consultancy. Book a call [https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf], or get in touch [alex.andorra@pymc-labs.io]!

 * Intro to Bayes Course [https://topmate.io/alex_andorra/503302] (first 2 lessons free)
 * Advanced Regression Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free)


Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work [https://bababrinkman.com/]!

Visit our Patreon page [https://www.patreon.com/learnbayesstats] 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, Pa

#147 Fast Approximate Inference without Convergence Worries, with Martin Ingram

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 Paris. 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)! This podcast uses the following
third-party services for analysis: Podcorn - https://podcorn.com/privacy

Alexandre ANDORRA

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