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 deep learning is a growing field with many challenges.
• Current research focuses on applying Bayesian methods to neural networks.
• Diffusion methods are emerging as a new approach for uncertainty quantification.
• The integration of machine learning tools into Bayesian models is a key area of research.
• The complexity of Bayesian neural networks poses significant computational challenges.
• Future research will focus on improving methods for uncertainty quantification. Generalized Bayesian inference offers a more robust approach to uncertainty.
• Uncertainty quantification is crucial in fields like medicine and epidemiology.
• Detecting out-of-distribution examples is essential for model reliability.
• Exploration-exploitation trade-off is vital in reinforcement learning.
• Marginal likelihood can be misleading for model selection.
• The integration of Bayesian methods in LLMs presents unique challenges.

Chapters:

00:00 Introduction to Bayesian Deep Learning

03:12 Panelist Introductions and Backgrounds

10:37 Current Research and Challenges in Bayesian Deep Learning

18:04 Contrasting Approaches: Bayesian vs. Machine Learning

26:09 Tools and Techniques for Bayesian Deep Learning

31:18 Innovative Methods in Uncertainty Quantification

36:23 Generalized Bayesian Inference and Its Implications

41:38 Robust Bayesian Inference and Gaussian Processes

44:24 Software Development in Bayesian Statistics

46:51 Understanding Uncertainty in Language Models

50:03 Hallucinations in Language Models

53:48 Bayesian Neural Networks vs Traditional Neural Networks

58:00 Challenges with Likelihood Assumptions

01:01:22 Practical Applications of Uncertainty Quantification

01:04:33 Meta Decision-Making with Uncertainty

01:06:50 Exploring Bayesian Priors in Neural Networks

01:09:17 Model Complexity and Data Signal

01:12:10 Marginal Likelihood and Model Selection

01:15:03 Implementing Bayesian Methods in LLMs

01:19:21 Out-of-Distribution Detection in LLMs

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,...

Today’s clip is from episode 137 of the podcast, with Robert Ness.

Alex and Robert discuss the intersection of causal inference and deep learning, emphasizing the importance of understanding causal concepts in statistical modeling.

The discussion also covers the evolution of probabilistic machine learning, the role of inductive biases, and the potential of large language models in causal analysis, highlighting their ability to translate natural language into formal causal queries.

Get the full conversation here.

Attend Alex's tutorial at PyData Berlin: A Beginner's Guide to State Space Modeling

• 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:

• Causal assumptions are crucial for statistical modeling.
• Deep learning can be integrated with causal models.
• Statistical rigor is essential in evaluating LLMs.
• Causal representation learning is a growing field.
• Inductive biases in AI should match key mechanisms.
• Causal AI can improve decision-making processes.
• The future of AI lies in understanding causal relationships.

Chapters:

00:00 Introduction to Causal AI and Its Importance

16:34 The Journey to Writing Causal AI

28:05 Integrating Graphical Causality with Deep Learning

40:10 The Evolution of Probabilistic Machine Learning

44:34 Practical Applications of Causal AI with LLMs

49:48 Exploring Multimodal Models and Causality

56:15 Tools and Frameworks for Causal AI

01:03:19 Statistical Rigor in Evaluating LLMs

01:12:22 Causal Thinking in Real-World Deployments

01:19:52 Trade-offs in Generative Causal Models

01:25:14 Future of Causal Generative Modeling

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...

Today’s clip is from episode 136 of the podcast, with Haavard Rue & Janet van Niekerk.

Alex, Haavard and Janet explore the world of Bayesian inference with INLA, a fast and deterministic method that revolutionizes how we handle large datasets and complex models.

Discover the power of INLA, and why it can make your models go much faster! Get the full conversation 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:

• INLA is a fast, deterministic method for Bayesian inference.
• INLA is particularly useful for large datasets and complex models.
• The R INLA package is widely used for implementing INLA methodology.
• INLA has been applied in various fields, including epidemiology and air quality control.
• Computational challenges in INLA are minimal compared to MCMC methods.
• The Smart Gradient method enhances the efficiency of INLA.
• INLA can handle various likelihoods, not just Gaussian.
• SPDs allow for more efficient computations in spatial modeling.
• The new INLA methodology scales better for large datasets, especially in medical imaging.
• Priors in Bayesian models can significantly impact the results and should be chosen carefully.
• Penalized complexity priors (PC priors) help prevent overfitting in models.
• Understanding the underlying mathematics of priors is crucial for effective modeling.
• The integration of GPUs in computational methods is a key future direction for INLA.
• The development of new sparse solvers is essential for handling larger models efficiently.

Chapters:

06:06 Understanding INLA: A Comparison with MCMC

08:46 Applications of INLA in Real-World Scenarios

11:58 Latent Gaussian Models and Their Importance

15:12 Impactful Applications of INLA in Health and Environment

18:09 Computational Challenges and Solutions in INLA

21:06 Stochastic Partial Differential Equations in Spatial Modeling

23:55 Future Directions and Innovations in INLA

39:51 Exploring Stochastic Differential Equations

43:02 Advancements in INLA Methodology

50:40 Getting Started with INLA

56:25 Understanding Priors in Bayesian Models

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

Get 10% off Hugo's "Building LLM Applications for Data Scientists and Software Engineers" online course!

Today’s clip is from episode 135 of the podcast, with Teemu Säilynoja.

Alex and Teemu discuss the importance of simulation-based calibration (SBC). They explore the practical implementation of SBC in probabilistic programming languages, the challenges faced in developing SBC methods, and the significance of both prior and posterior SBC in ensuring model reliability.

The discussion emphasizes the need for careful model implementation and inference algorithms to achieve accurate calibration.

Get the full conversation 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:

• Teemu focuses on calibration assessments and predictive checking in Bayesian workflows.
• Simulation-based calibration (SBC) checks model implementation
• SBC involves drawing realizations from prior and generating prior predictive data.
• Visual predictive checking is crucial for assessing model predictions.
• Prior predictive checks should be done before looking at data.
• Posterior SBC focuses on the area of parameter space most relevant to the data.
• Challenges in SBC include inference time.
• Visualizations complement numerical metrics in Bayesian modeling.
• Amortized Bayesian inference benefits from SBC for quick posterior checks. The calibration of Bayesian models is more intuitive than Frequentist models.
• Choosing the right visualization depends on data characteristics.
• Using multiple visualization methods can reveal different insights.
• Visualizations should be viewed as models of the data.
• Goodness of fit tests can enhance visualization accuracy.
• Uncertainty visualization is crucial but often overlooked.

Chapters:

09:53 Understanding Simulation-Based Calibration (SBC)

15:03 Practical Applications of SBC in Bayesian Modeling

22:19 Challenges in Developing Posterior SBC

29:41 The Role of SBC in Amortized Bayesian Inference

33:47 The Importance of Visual Predictive Checking

36:50 Predictive Checking and Model Fitting

38:08 The Importance of Visual Checks

40:54 Choosing Visualization Types

49:06 Visualizations as Models

55:02 Uncertainty Visualization in Bayesian Modeling

01:00:05 Future Trends in Probabilistic Modeling

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand...

ICYMI, I'll be in London next week, for a live episode of the Learning Bayesian Statistics podcast 🍾

Come say hi on June 24 at Imperial College London! We'll be talking about uncertainty quantification — not just in theory, but in the messy, practical reality of building models that are supposed to work in the real world.

🎟️ Get your tickets!

Some of the questions we’ll unpack:

🔍 Why is it so hard to model uncertainty reliably?

⚠️ How do overconfident models break things in production?

🧠 What tools and frameworks help today?

🔄 What do we need to rethink if we want robust ML over the next decade?

Joining me on stage: the brilliant Mélodie Monod, Yingzhen Li and François-Xavier Briol -- researchers doing cutting-edge work on these questions, across Bayesian methods, statistical learning, and real-world ML deployment.

A huge thank you to Oliver Ratmann for setting this up!

📍 Imperial-X, White City Campus (Room LRT 608)

🗓️ June 24, 11:30–13:00

🎙️ Doors open at 11:30 — we start at noon sharp

Come say hi, ask hard questions, and be part of the recording.

🎟️ Get your tickets!

• 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, Ero Carrera, Giuliano Cruz, Tim Gasser, 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, Steven Rowland, 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, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh,...