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Today’s clip is from episode 141 of the podcast, with Sam Witty.

Alex and Sam discuss the ChiRho project, delving into the intricacies of causal inference, particularly focusing on Do-Calculus, regression discontinuity designs, and Bayesian structural causal inference.

They explain ChiRho's design philosophy, emphasizing its modular and extensible nature, and highlights the importance of efficient estimation in causal inference, making complex statistical methods accessible to users without extensive expertise.

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!

• Get early access to Alex's next live-cohort courses!
• Enroll in the Causal AI workshop, to learn live with Alex (15% off if you're a Patron of the show)

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 inference is crucial for understanding the impact of interventions in various fields.
• ChiRho is a causal probabilistic programming language that bridges mechanistic and data-driven models.
• ChiRho allows for easy manipulation of causal models and counterfactual reasoning.
• The design of ChiRho emphasizes modularity and extensibility for diverse applications.
• Causal inference requires careful consideration of assumptions and model structures.
• Real-world applications of causal inference can lead to significant insights in science and engineering.
• Collaboration and communication are key in translating causal questions into actionable models.
• The future of causal inference lies in integrating probabilistic programming with scientific discovery.

Chapters:

05:53 Bridging Mechanistic and Data-Driven Models

09:13 Understanding Causal Probabilistic Programming

12:10 ChiRho and Its Design Principles

15:03 ChiRho’s Functionality and Use Cases

17:55 Counterfactual Worlds and Mediation Analysis

20:47 Efficient Estimation in ChiRho

24:08 Future Directions for Causal AI

50:21 Understanding the Do-Operator in Causal Inference

56:45 ChiRho’s Role in Causal Inference and Bayesian Modeling

01:01:36 Roadmap and Future Developments for ChiRho

01:05:29 Real-World Applications of Causal Probabilistic Programming

01:10:51 Challenges in Causal Inference Adoption

01:11:50 The Importance of Causal Claims in Research

01:18:11 Bayesian Approaches to Causal Inference

01:22:08 Combining Gaussian Processes with Causal Inference

01:28:27 Future Directions in Probabilistic Programming and Causal Inference

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...

Today’s clip is from episode 140 of the podcast, with Ron Yurko.

Alex and Ron discuss the challenges of model deployment, and the complexities of modeling player contributions in team sports like soccer and football.

They emphasize the importance of understanding replacement levels, the Going Deep framework in football analytics, and the need for proper modeling of expected points.

Additionally, they share insights on teaching Bayesian modeling to students and the difficulties they face in grasping the concepts of model writing and application.

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.

Get early access to Alex's next live-cohort courses!

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:

• Teaching students to write out their own models is crucial.
• Developing a sports analytics portfolio is essential for aspiring analysts.
• Modeling expectations in sports analytics can be misleading.
• Tracking data can significantly improve player performance models.
• Ron encourages students to engage in active learning through projects.
• The importance of understanding the dependency structure in data is vital.
• Ron aims to integrate more diverse sports analytics topics into his teaching.

Chapters:

03:51 The Journey into Sports Analytics

15:20 The Evolution of Bayesian Statistics in Sports

26:01 Innovations in NFL WAR Modeling

39:23 Causal Modeling in Sports Analytics

46:29 Defining Replacement Levels in Sports

48:26 The Going Deep Framework and Big Data in Football

52:47 Modeling Expectations in Football Data

55:40 Teaching Statistical Concepts in Sports Analytics

01:01:54 The Importance of Model Building in Education

01:04:46 Statistical Thinking in Sports Analytics

01:10:55 Innovative Research in Player Movement

01:15:47 Exploring Data Needs in American Football

01:18:43 Building a Sports Analytics Portfolio

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,...

Today’s clip is from episode 139 of the podcast, with with Max Balandat.

Alex and Max discuss the integration of BoTorch with PyTorch, exploring its applications in Bayesian optimization and Gaussian processes. They highlight the advantages of using GPyTorch for structured matrices and the flexibility it offers for research.

The discussion also covers the motivations behind building BoTorch, the importance of open-source culture at Meta, and the role of PyTorch in modern machine learning.

Get the full discussion 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:

• BoTorch is designed for researchers who want flexibility in Bayesian optimization.
• The integration of BoTorch with PyTorch allows for differentiable programming.
• Scalability at Meta involves careful software engineering practices and testing.
• Open-source contributions enhance the development and community engagement of BoTorch.
• LLMs can help incorporate human knowledge into optimization processes.
• Max emphasizes the importance of clear communication of uncertainty to stakeholders.
• The role of a researcher in industry is often more application-focused than in academia.
• Max's team at Meta works on adaptive experimentation and Bayesian optimization.

Chapters:

08:51 Understanding BoTorch

12:12 Use Cases and Flexibility of BoTorch

15:02 Integration with PyTorch and GPyTorch

17:57 Practical Applications of BoTorch

20:50 Open Source Culture at Meta and BoTorch's Development

43:10 The Power of Open Source Collaboration

47:49 Scalability Challenges at Meta

51:02 Balancing Depth and Breadth in Problem Solving

55:08 Communicating Uncertainty to Stakeholders

01:00:53 Learning from Missteps in Research

01:05:06 Integrating External Contributions into BoTorch

01:08:00 The Future of Optimization with 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, 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,...

Today’s clip is from episode 138 of the podcast, with Mélodie Monod, François-Xavier Briol and Yingzhen Li.

During this live show at Imperial College London, Alex and his guests delve into the complexities and advancements in Bayesian deep learning, focusing on uncertainty quantification, the integration of machine learning tools, and the challenges faced in simulation-based inference.

The speakers discuss their current projects, the evolution of Bayesian models, and the need for better computational tools in the field.

Get the full discussion 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:

• 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,...