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Explore every episode of the podcast Learning Bayesian Statistics

Dive into the complete episode list for Learning Bayesian Statistics. Each episode is cataloged with detailed descriptions, making it easy to find and explore specific topics. Keep track of all episodes from your favorite podcast and never miss a moment of insightful content.

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TitlePub. DateDuration
#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte02 Oct 202501:10:28

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:

  • BART as a core tool: Gabriel explains how Bayesian Additive Regression Trees provide robust uncertainty quantification and serve as a reliable baseline model in many domains.
  • Rust for performance: His Rust re-implementation of BART dramatically improves speed and scalability, making it feasible for larger datasets and real-world IoT applications.
  • Strengths and trade-offs: BART avoids overfitting and handles missing data gracefully, though it is slower than other tree-based approaches.
  • Big data meets Bayes: Gabriel shares strategies for applying Bayesian methods with big data, including when variational inference helps balance scale with rigor.
  • Optimization and decision-making: He highlights how BART models can be embedded into optimization frameworks, opening doors for sequential decision-making.
  • Open source matters: Gabriel emphasizes the importance of communities like PyMC and Bambi, encouraging newcomers to start with small contributions.

Chapters:

05:10 – From economics to IoT and Bayesian statistics

18:55 – Introduction to BART (Bayesian Additive Regression Trees)

24:40 – Re-implementing BART in Rust for speed and scalability

32:05 – Comparing BART with Gaussian Processes and other tree methods

39:50 – Strengths and limitations of BART

47:15 – Handling missing data and different likelihoods

54:30 – Variational inference and big data challenges

01:01:10 – Embedding BART into optimization and decision-making frameworks

01:08:45 – Open source, PyMC, and community support

01:15:20 – Advice for newcomers

01:20:55 – Future of BART, Rust, and probabilistic programming

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...

BITESIZE | How Probability Becomes Causality?24 Sep 202500:22:03

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

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.


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.

#141 AI Assisted Causal Inference, with Sam Witty18 Sep 202501:37:47

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:

  • 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...

BITESIZE | How to Think Causally About Your Models?10 Sep 202500:24:01

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

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.


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.

#140 NFL Analytics & Teaching Bayesian Stats, with Ron Yurko03 Sep 202501:33:01

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!


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,...

BITESIZE | Is Bayesian Optimization the Answer?27 Aug 202500:25:13

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 


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.

#139 Efficient Bayesian Optimization in PyTorch, with Max Balandat20 Aug 202501:25:23

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:

  • 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,...

BITESIZE | What's Missing in Bayesian Deep Learning?13 Aug 202500:20:34

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 


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.

#138 Quantifying Uncertainty in Bayesian Deep Learning, Live from Imperial College London06 Aug 202501:23:10

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:

  • 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,...

BITESIZE | Practical Applications of Causal AI with LLMs, with Robert Ness30 Jul 202500:25:28

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 


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.

#137 Causal AI & Generative Models, with Robert Ness23 Jul 202501:38:19

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:

  • 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...

BITESIZE | How to Make Your Models Faster, with Haavard Rue & Janet van Niekerk16 Jul 202500:17:53

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.


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.

#136 Bayesian Inference at Scale: Unveiling INLA, with Haavard Rue & Janet van Niekerk09 Jul 202501:17:37

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:

  • 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

BITESIZE | Understanding Simulation-Based Calibration, with Teemu Säilynoja04 Jul 202500:21:14

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.


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.

#135 Bayesian Calibration and Model Checking, with Teemu Säilynoja25 Jun 202501:12:13

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:

  • 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...

Live Show Announcement | Come Meet Me in London!19 Jun 202500:03:04

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!


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,...

BITESIZE | Exploring Dynamic Regression Models, with David Kohns18 Jun 202500:14:34

Today’s clip is from episode 134 of the podcast, with David Kohns.

Alex and David discuss the future of probabilistic programming, focusing on advancements in time series modeling, model selection, and the integration of AI in prior elicitation.

The discussion highlights the importance of setting appropriate priors, the challenges of computational workflows, and the potential of normalizing flows to enhance Bayesian inference.

Get the full discussion here.


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.

#134 Bayesian Econometrics, State Space Models & Dynamic Regression, with David Kohns10 Jun 202501:40:55

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:

  • Setting appropriate priors is crucial to avoid overfitting in models.
  • R-squared can be used effectively in Bayesian frameworks for model evaluation.
  • Dynamic regression can incorporate time-varying coefficients to capture changing relationships.
  • Predictively consistent priors enhance model interpretability and performance.
  • Identifiability is a challenge in time series models.
  • State space models provide structure compared to Gaussian processes.
  • Priors influence the model's ability to explain variance.
  • Starting with simple models can reveal interesting dynamics.
  • Understanding the relationship between states and variance is key.
  • State-space models allow for dynamic analysis of time series data.
  • AI can enhance the process of prior elicitation in statistical models.

Chapters:

10:09 Understanding State Space Models

14:53 Predictively Consistent Priors

20:02 Dynamic Regression and AR Models

25:08 Inflation Forecasting

50:49 Understanding Time Series Data and Economic Analysis

57:04 Exploring Dynamic Regression Models

01:05:52 The Role of Priors

01:15:36 Future Trends in Probabilistic Programming

01:20:05 Innovations in Bayesian Model Selection

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...

BITESIZE | Why Your Models Might Be Wrong & How to Fix it, with Sean Pinkney & Adrian Seyboldt04 Jun 202500:17:04

Today’s clip is from episode 133 of the podcast, with Sean Pinkney & Adrian Seyboldt.

The conversation delves into the concept of Zero-Sum Normal and its application in statistical modeling, particularly in hierarchical models.

Alex, Sean and Adrian discuss the implications of using zero-sum constraints, the challenges of incorporating new data points, and the importance of distinguishing between sample and population effects.

They also explore practical solutions for making predictions based on population parameters and the potential for developing tools to facilitate these processes.

Get the full discussion here.


Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

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Transcript

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#133 Making Models More Efficient & Flexible, with Sean Pinkney & Adrian Seyboldt28 May 202501:12:12

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Takeaways:

  • Zero Sum constraints allow for better sampling and estimation in hierarchical models.
  • Understanding the difference between population and sample means is crucial.
  • A library for zero-sum normal effects would be beneficial.
  • Practical solutions can yield decent predictions even with limitations.
  • Cholesky parameterization can be adapted for positive correlation matrices.
  • Understanding the geometry of sampling spaces is crucial.
  • The relationship between eigenvalues and sampling is complex.
  • Collaboration and sharing knowledge enhance research outcomes.
  • Innovative approaches can simplify complex statistical problems.

Chapters:

03:35 Sean Pinkney's Journey to Bayesian Modeling

11:21 The Zero-Sum Normal Project Explained

18:52 Technical Insights on Zero-Sum Constraints

32:04 Handling New Elements in Bayesian Models

36:19 Understanding Population Parameters and Predictions

49:11 Exploring Flexible Cholesky Parameterization

01:07:23 Closing Thoughts and Future Directions

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, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, 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...

BITESIZE | How AI is Redefining Human Interactions, with Tom Griffiths21 May 202500:22:06

Today’s clip is from episode 132 of the podcast, with Tom Griffiths.

Tom and Alex Andorra discuss the fundamental differences between human intelligence and artificial intelligence, emphasizing the constraints that shape human cognition, such as limited data, computational resources, and communication bandwidth.

They explore how AI systems currently learn and the potential for aligning AI with human cognitive processes.

The discussion also delves into the implications of AI in enhancing human decision-making and the importance of understanding human biases to create more effective AI systems.

Get the full discussion here.


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Transcript

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#132 Bayesian Cognition and the Future of Human-AI Interaction, with Tom Griffiths13 May 202501:30:15

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Check out Hugo’s latest episode with Fei-Fei Li, on How Human-Centered AI Actually Gets Built


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Takeaways:

  • Computational cognitive science seeks to understand intelligence mathematically.
  • Bayesian statistics is crucial for understanding human cognition.
  • Inductive biases help explain how humans learn from limited data.
  • Eliciting prior distributions can reveal implicit beliefs.
  • The wisdom of individuals can provide richer insights than averaging group responses.
  • Generative AI can mimic human cognitive processes.
  • Human intelligence is shaped by constraints of data, computation, and communication.
  • AI systems operate under different constraints than human cognition. Human intelligence differs fundamentally from machine intelligence.
  • Generative AI can complement and enhance human learning.
  • AI systems currently lack intrinsic human compatibility.
  • Language training in AI helps align its understanding with human perspectives.
  • Reinforcement learning from human feedback can lead to misalignment of AI goals.
  • Representational alignment can improve AI's understanding of human concepts.
  • AI can help humans make better decisions by providing relevant information.
  • Research should focus on solving problems rather than just methods.

Chapters:

00:00 Understanding Computational Cognitive Science

13:52 Bayesian Models and Human Cognition

29:50 Eliciting Implicit Prior Distributions

38:07 The Relationship Between Human and AI Intelligence

45:15 Aligning Human and Machine Preferences

50:26 Innovations in AI and Human Interaction

55:35 Resource Rationality in Decision Making

01:00:07 Language Learning in AI Models

BITESIZE | Hacking Bayesian Models for Better Performance, with Luke Bornn07 May 202500:13:35

Today’s clip is from episode 131 of the podcast, with Luke Bornn.

Luke and Alex discuss the application of generative models in sports analytics. They emphasize the importance of Bayesian modeling to account for uncertainty and contextual variations in player data.

The discussion also covers the challenges of balancing model complexity with computational efficiency, the innovative ways to hack Bayesian models for improved performance, and the significance of understanding model fitting and discretization in statistical modeling.

Get the full discussion here.


Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

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Transcript

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#131 Decision-Making Under High Uncertainty, with Luke Bornn30 Apr 202501:31:46

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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, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, 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 and Suyog Chandramouli.

Takeaways:

  • Player tracking data revolutionized sports analytics.
  • Decision-making in sports involves managing uncertainty and budget constraints.
  • Luke emphasizes the importance of portfolio optimization in team management.
  • Clubs with high budgets can afford inefficiencies in player acquisition.
  • Statistical methods provide a probabilistic approach to player value.
  • Removing human bias is crucial in sports decision-making.
  • Understanding player performance distributions aids in contract decisions.
  • The goal is to maximize performance value per dollar spent.
  • Model validation in sports requires focusing on edge cases.
BITESIZE | Real-World Applications of Models in Public Health, with Adam Kucharski23 Apr 202500:16:26

Today’s clip is from episode 130 of the podcast, with epidemiological modeler Adam Kucharski.

This conversation explores the critical role of patient modeling during the COVID-19 pandemic, highlighting how these models informed public health decisions and the relationship between modeling and policy.

The discussion emphasizes the need for improved communication and understanding of data among the public and policymakers.

Get the full discussion here.


Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

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Transcript

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#130 The Real-World Impact of Epidemiological Models, with Adam Kucharski16 Apr 202501:09:05

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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, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, 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 and Suyog Chandramouli.

Takeaways:

  • Epidemiology requires a blend of mathematical and statistical understanding.
  • Models are essential for informing public health decisions during epidemics.
  • The COVID-19 pandemic highlighted the importance of rapid modeling.
  • Misconceptions about data can lead to misunderstandings in public health.
  • Effective communication is crucial for conveying complex epidemiological concepts.
  • Epidemic thinking can be applied to various fields, including marketing and finance.
  • Public health policies should be informed by robust modeling and data analysis.
  • Automation can help streamline data analysis in epidemic response.
  • Understanding the limitations of models...
BITESIZE | The Why & How of Bayesian Deep Learning, with Vincent Fortuin09 Apr 202500:11:45

Today’s clip is from episode 129 of the podcast, with AI expert and researcher Vincent Fortuin.

This conversation delves into the intricacies of Bayesian deep learning, contrasting it with traditional deep learning and exploring its applications and challenges.

Get the full discussion at https://learnbayesstats.com/episode/129-bayesian-deep-learning-ai-for-science-vincent-fortuin


Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

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Transcript

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#129 Bayesian Deep Learning & AI for Science with Vincent Fortuin02 Apr 202501:02:43

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Takeaways:

  • The hype around AI in science often fails to deliver practical results.
  • Bayesian deep learning combines the strengths of deep learning and Bayesian statistics.
  • Fine-tuning LLMs with Bayesian methods improves prediction calibration.
  • There is no single dominant library for Bayesian deep learning yet.
  • Real-world applications of Bayesian deep learning exist in various fields.
  • Prior knowledge is crucial for the effectiveness of Bayesian deep learning.
  • Data efficiency in AI can be enhanced by incorporating prior knowledge.
  • Generative AI and Bayesian deep learning can inform each other.
  • The complexity of a problem influences the choice between Bayesian and traditional deep learning.
  • Meta-learning enhances the efficiency of Bayesian models.
  • PAC-Bayesian theory merges Bayesian and frequentist ideas.
  • Laplace inference offers a cost-effective approximation.
  • Subspace inference can optimize parameter efficiency.
  • Bayesian deep learning is crucial for reliable predictions.
  • Effective communication of uncertainty is essential.
  • Realistic benchmarks are needed for Bayesian methods
  • Collaboration and communication in the AI community are vital.

Chapters:

00:00 Introduction to Bayesian Deep Learning

06:12 Vincent's Journey into Machine Learning

12:42 Defining Bayesian Deep Learning

17:23 Current Landscape of Bayesian Libraries

22:02 Real-World Applications of Bayesian Deep Learning

24:29 When to Use Bayesian Deep Learning

29:36 Data Efficient AI and Generative Modeling

31:59 Exploring Generative AI and Meta-Learning

34:19 Understanding Bayesian Deep Learning and Prior Knowledge

39:01 Algorithms for Bayesian Deep Learning Models

43:25 Advancements in Efficient Inference Techniques

49:35 The Future of AI Models and Reliability

52:47 Advice for Aspiring Researchers in AI

56:06 Future Projects and Research Directions

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade,...

#128 Building a Winning Data Team in Football, with Matt Penn19 Mar 202500:58:11

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Takeaways:

  • Matt emphasizes the importance of Bayesian statistics in scenarios with limited data.
  • Communicating insights to coaches is a crucial skill for data analysts.
  • Building a data team requires understanding the needs of the coaching staff.
  • Player recruitment is a significant focus in football analytics.
  • The integration of data science in sports is still evolving.
  • Effective data modeling must consider the practical application in games.
  • Collaboration between data analysts and coaches enhances decision-making.
  • Having a robust data infrastructure is essential for efficient analysis.
  • The landscape of sports analytics is becoming increasingly competitive. 
  • Player recruitment involves analyzing various data models.
  • Biases in traditional football statistics can skew player evaluations.
  • Statistical techniques should leverage the structure of football data.
  • Tracking data opens new avenues for understanding player movements.
  • The role of data analysis in football will continue to grow.
  • Aspiring analysts should focus on curiosity and practical experience.

Chapters:

00:00 Introduction to Football Analytics and Matt's Journey

04:54 The Role of Bayesian Methods in Football

10:20 Challenges in Communicating Data Insights

17:03 Building Relationships with Coaches

22:09 The Structure of the Data Team at Como

26:18 Focus on Player Recruitment and Transfer Strategies

28:48 January Transfer Window Insights

30:54 Biases in Football Data Analysis

34:11 Comparative Analysis of Men's and Women's Football

36:55 Statistical Techniques in Football Analysis

42:48 The Impact of Tracking Data on Football Analysis

45:49 The Future of Data-Driven Football Strategies

47:27 Advice for Aspiring Football Analysts

#127 Saving Sharks... with Python, Causal Inference and Aaron MacNeil05 Mar 202501:04:08

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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, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, 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 and Michael Cao.

Takeaways:

  • Sharks play a crucial role in maintaining healthy ocean ecosystems.
  • Bayesian statistics are particularly useful in data-poor environments like ecology.
  • Teaching Bayesian statistics requires a shift in mindset from traditional statistical methods.
  • The shark meat trade is significant and often overlooked.
  • Ray meat trade is as large as shark meat trade, with specific markets dominating.
  • Understanding the ecological roles of species is essential for effective conservation.
  • Causal language is important in ecological research and should be encouraged.
  • Evidence-driven decision-making is crucial in balancing human and ecological needs.
  • Expert opinions are...
#126 MMM, CLV & Bayesian Marketing Analytics, with Will Dean19 Feb 202500:54:47

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Takeaways:

  • Marketing analytics is crucial for understanding customer behavior.
  • PyMC Marketing offers tools for customer lifetime value analysis.
  • Media mix modeling helps allocate marketing spend effectively.
  • Customer Lifetime Value (CLV) models are essential for understanding long-term customer behavior.
  • Productionizing models is essential for real-world applications.
  • Productionizing models involves challenges like model artifact storage and version control.
  • MLflow integration enhances model tracking and management.
  • The open-source community fosters collaboration and innovation.
  • Understanding time series is vital in marketing analytics.
  • Continuous learning is key in the evolving field of data science.

Chapters:

00:00 Introduction to Will Dean and His Work

10:48 Diving into PyMC Marketing

17:10 Understanding Media Mix Modeling

25:54 Challenges in Productionizing Models

35:27 Exploring Customer Lifetime Value Models

44:10 Learning and Development in Data Science

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,...

#125 Bayesian Sports Analytics & The Future of PyMC, with Chris Fonnesbeck05 Feb 202500:58:15

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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, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, 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 and Mike Loncaric.

Takeaways:

  • The evolution of sports modeling is tied to the availability of high-frequency data.
  • Bayesian methods are valuable in handling messy, hierarchical data.
  • Communication between data scientists and decision-makers is crucial for effective model use.
  • Models are often wrong, and learning from mistakes is part of the process.
  • Simplicity in models can sometimes yield better results than complexity.
  • The integration of analytics in sports is still developing, with opportunities in various sports.
  • Transparency in research and development teams enhances decision-making.
  • Understanding uncertainty in models is essential for informed decisions.
  • The balance between point estimates and full distributions is a...
#124 State Space Models & Structural Time Series, with Jesse Grabowski22 Jan 202501:35:43

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Takeaways:

  • Bayesian statistics offers a robust framework for econometric modeling.
  • State space models provide a comprehensive way to understand time series data.
  • Gaussian random walks serve as a foundational model in time series analysis.
  • Innovations represent external shocks that can significantly impact forecasts.
  • Understanding the assumptions behind models is key to effective forecasting.
  • Complex models are not always better; simplicity can be powerful.
  • Forecasting requires careful consideration of potential disruptions. Understanding observed and hidden states is crucial in modeling.
  • Latent abilities can be modeled as Gaussian random walks.
  • State space models can be highly flexible and diverse.
  • Composability allows for the integration of different model components.
  • Trends in time series should reflect real-world dynamics.
  • Seasonality can be captured through Fourier bases.
  • AR components help model residuals in time series data.
  • Exogenous regression components can enhance state space models.
  • Causal analysis in time series often involves interventions and counterfactuals.
  • Time-varying regression allows for dynamic relationships between variables.
  • Kalman filters were originally developed for tracking rockets in space.
  • The Kalman filter iteratively updates beliefs based on new data.
  • Missing data can be treated as hidden states in the Kalman filter framework.
  • The Kalman filter is a practical application of Bayes' theorem in a sequential context.
  • Understanding the dynamics of systems is crucial for effective modeling.
  • The state space module in PyMC simplifies complex time series modeling tasks.

Chapters:

00:00 Introduction to Jesse Krabowski and Time Series Analysis

04:33 Jesse's Journey into Bayesian Statistics

10:51 Exploring State Space Models

18:28 Understanding State Space Models and Their Components

#123 BART & The Future of Bayesian Tools, with Osvaldo Martin10 Jan 202501:32:13

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Takeaways:

  • BART models are non-parametric Bayesian models that approximate functions by summing trees.
  • BART is recommended for quick modeling without extensive domain knowledge.
  • PyMC-BART allows mixing BART models with various likelihoods and other models.
  • Variable importance can be easily interpreted using BART models.
  • PreliZ aims to provide better tools for prior elicitation in Bayesian statistics.
  • The integration of BART with Bambi could enhance exploratory modeling.
  • Teaching Bayesian statistics involves practical problem-solving approaches.
  • Future developments in PyMC-BART include significant speed improvements.
  • Prior predictive distributions can aid in understanding model behavior.
  • Interactive learning tools can enhance understanding of statistical concepts.
  • Integrating PreliZ with PyMC improves workflow transparency.
  • Arviz 1.0 is being completely rewritten for better usability.
  • Prior elicitation is crucial in Bayesian modeling.
  • Point intervals and forest plots are effective for visualizing complex data.

Chapters:

00:00 Introduction to Osvaldo Martin and Bayesian Statistics

08:12 Exploring Bayesian Additive Regression Trees (BART)

18:45 Prior Elicitation and the PreliZ Package

29:56 Teaching Bayesian Statistics and Future Directions

45:59 Exploring Prior Predictive Distributions

52:08 Interactive Modeling with PreliZ

54:06 The Evolution of ArviZ

01:01:23 Advancements in ArviZ 1.0

01:06:20 Educational Initiatives in Bayesian Statistics

01:12:33 The Future of Bayesian Methods

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...

#122 Learning and Teaching in the Age of AI, with Hugo Bowne-Anderson26 Dec 202401:23:10

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Takeaways:

  • Effective data science education requires feedback and rapid iteration.
  • Building LLM applications presents unique challenges and opportunities.
  • The software development lifecycle for AI differs from traditional methods.
  • Collaboration between data scientists and software engineers is crucial.
  • Hugo's new course focuses on practical applications of LLMs.
  • Continuous learning is essential in the fast-evolving tech landscape.
  • Engaging learners through practical exercises enhances education.
  • POC purgatory refers to the challenges faced in deploying LLM-powered software.
  • Focusing on first principles can help overcome integration issues in AI.
  • Aspiring data scientists should prioritize problem-solving over specific tools.
  • Engagement with different parts of an organization is crucial for data scientists.
  • Quick paths to value generation can help gain buy-in for data projects.
  • Multimodal models are an exciting trend in AI development.
  • Probabilistic programming has potential for future growth in data science.
  • Continuous learning and curiosity are vital in the evolving field of data science.

Chapters:

09:13 Hugo's Journey in Data Science and Education

14:57 The Appeal of Bayesian Statistics

19:36 Learning and Teaching in Data Science

24:53 Key Ingredients for Effective Data Science Education

28:44 Podcasting Journey and Insights

36:10 Building LLM Applications: Course Overview

42:08 Navigating the Software Development Lifecycle

48:06 Overcoming Proof of Concept Purgatory

55:35 Guidance for Aspiring Data Scientists

01:03:25 Exciting Trends in Data Science and AI

01:10:51 Balancing Multiple Roles in Data Science

01:15:23 Envisioning Accessible Data Science for All

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim

#121 Exploring Bayesian Structural Equation Modeling, with Nathaniel Forde11 Dec 202401:08:13

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Takeaways:

  • CFA is commonly used in psychometrics to validate theoretical constructs.
  • Theoretical structure is crucial in confirmatory factor analysis.
  • Bayesian approaches offer flexibility in modeling complex relationships.
  • Model validation involves both global and local fit measures.
  • Sensitivity analysis is vital in Bayesian modeling to avoid skewed results.
  • Complex models should be justified by their ability to answer specific questions.
  • The choice of model complexity should balance fit and theoretical relevance. Fitting models to real data builds confidence in their validity.
  • Divergences in model fitting indicate potential issues with model specification.
  • Factor analysis can help clarify causal relationships between variables.
  • Survey data is a valuable resource for understanding complex phenomena.
  • Philosophical training enhances logical reasoning in data science.
  • Causal inference is increasingly recognized in industry applications.
  • Effective communication is essential for data scientists.
  • Understanding confounding is crucial for accurate modeling.

Chapters:

10:11 Understanding Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA)

20:11 Application of SEM and CFA in HR Analytics

30:10 Challenges and Advantages of Bayesian Approaches in SEM and CFA

33:58 Evaluating Bayesian Models

39:50 Challenges in Model Building

44:15 Causal Relationships in SEM and CFA

49:01 Practical Applications of SEM and CFA

51:47 Influence of Philosophy on Data Science

54:51 Designing Models with Confounding in Mind

57:39 Future Trends in Causal Inference

01:00:03 Advice for Aspiring Data Scientists

01:02:48 Future Research Directions

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,

#120 Innovations in Infectious Disease Modeling, with Liza Semenova & Chris Wymant27 Nov 202401:01:39

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Takeaways:

  • Epidemiology focuses on health at various scales, while biology often looks at micro-level details.
  • Bayesian statistics helps connect models to data and quantify uncertainty.
  • Recent advancements in data collection have improved the quality of epidemiological research.
  • Collaboration between domain experts and statisticians is essential for effective research.
  • The COVID-19 pandemic has led to increased data availability and international cooperation.
  • Modeling infectious diseases requires understanding complex dynamics and statistical methods.
  • Challenges in coding and communication between disciplines can hinder progress.
  • Innovations in machine learning and neural networks are shaping the future of epidemiology.
  • The importance of understanding the context and limitations of data in research. 

Chapters:

00:00 Introduction to Bayesian Statistics and Epidemiology

03:35 Guest Backgrounds and Their Journey

10:04 Understanding Computational Biology vs. Epidemiology

16:11 The Role of Bayesian Statistics in Epidemiology

21:40 Recent Projects and Applications in Epidemiology

31:30...

#119 Causal Inference, Fiction Writing and Career Changes, with Robert Kubinec13 Nov 202401:25:01

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Takeaways:

  • Bob's research focuses on corruption and political economy.
  • Measuring corruption is challenging due to the unobservable nature of the behavior.
  • The challenge of studying corruption lies in obtaining honest data.
  • Innovative survey techniques, like randomized response, can help gather sensitive data.
  • Non-traditional backgrounds can enhance statistical research perspectives.
  • Bayesian methods are particularly useful for estimating latent variables.
  • Bayesian methods shine in situations with prior information.
  • Expert surveys can help estimate uncertain outcomes effectively.
  • Bob's novel, 'The Bayesian Hitman,' explores academia through a fictional lens.
  • Writing fiction can enhance academic writing skills and creativity.
  • The importance of community in statistics is emphasized, especially in the Stan community.
  • Real-time online surveys could revolutionize data collection in social science.

Chapters:

00:00 Introduction to Bayesian Statistics and Bob Kubinec

06:01 Bob's Academic Journey and Research Focus

12:40 Measuring Corruption: Challenges and Methods

18:54 Transition from Government to Academia

26:41 The Influence of Non-Traditional Backgrounds in Statistics

34:51 Bayesian Methods in Political Science Research

42:08 Bayesian Methods in COVID Measurement

51:12 The Journey of Writing a Novel

01:00:24 The Intersection of Fiction and Academia

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,...

#118 Exploring the Future of Stan, with Charles Margossian & Brian Ward30 Oct 202400:58:51

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Takeaways:

  • User experience is crucial for the adoption of Stan.
  • Recent innovations include adding tuples to the Stan language, new features and improved error messages.
  • Tuples allow for more efficient data handling in Stan.
  • Beginners often struggle with the compiled nature of Stan.
  • Improving error messages is crucial for user experience.
  • BridgeStan allows for integration with other programming languages and makes it very easy for people to use Stan models.
  • Community engagement is vital for the development of Stan.
  • New samplers are being developed to enhance performance.
  • The future of Stan includes more user-friendly features.

Chapters:

00:00 Introduction to the Live Episode

02:55 Meet the Stan Core Developers

05:47 Brian Ward's Journey into Bayesian Statistics

09:10 Charles Margossian's Contributions to Stan

11:49 Recent Projects and Innovations in Stan

15:07 User-Friendly Features and Enhancements

18:11 Understanding Tuples and Their Importance

21:06 Challenges for Beginners in Stan

24:08 Pedagogical Approaches to Bayesian Statistics

30:54 Optimizing Monte Carlo Estimators

32:24 Reimagining Stan's Structure

34:21 The Promise of Automatic Reparameterization

35:49 Exploring BridgeStan

40:29 The Future of Samplers in Stan

43:45 Evaluating New Algorithms

47:01 Specific Algorithms for Unique Problems

50:00 Understanding Model Performance

54:21 The Impact of Stan on Bayesian Research

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...

#117 Unveiling the Power of Bayesian Experimental Design, with Desi Ivanova15 Oct 202401:13:12

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

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Takeaways:

  • Designing experiments is about optimal data gathering.
  • The optimal design maximizes the amount of information.
  • The best experiment reduces uncertainty the most.
  • Computational challenges limit the feasibility of BED in practice.
  • Amortized Bayesian inference can speed up computations.
  • A good underlying model is crucial for effective BED.
  • Adaptive experiments are more complex than static ones.
  • The future of BED is promising with advancements in AI.

Chapters:

00:00 Introduction to Bayesian Experimental Design

07:51 Understanding Bayesian Experimental Design

19:58 Computational Challenges in Bayesian Experimental Design

28:47 Innovations in Bayesian Experimental Design

40:43 Practical Applications of Bayesian Experimental Design

52:12 Future of Bayesian Experimental Design

01:01:17 Real-World Applications and Impact

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, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov,...

#116 Mastering Soccer Analytics, with Ravi Ramineni02 Oct 202401:32:46

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

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Takeaways:

  • Building an athlete management system and a scouting and recruitment platform are key goals in football analytics.
  • The focus is on informing training decisions, preventing injuries, and making smart player signings.
  • Avoiding false positives in player evaluations is crucial, and data analysis plays a significant role in making informed decisions.
  • There are similarities between different football teams, and the sport has social and emotional aspects. Transitioning from on-premises SQL servers to cloud-based systems is a significant endeavor in football analytics.
  • Analytics is a tool that aids the decision-making process and helps mitigate biases. The impact of analytics in soccer can be seen in the decline of long-range shots.
  • Collaboration and trust between analysts and decision-makers are crucial for successful implementation of analytics.
  • The limitations of available data in football analytics hinder the ability to directly measure decision-making on the field. 
  • Analyzing the impact of coaches in sports analytics is challenging due to the difficulty of separating their effect from other factors. Current data limitations make it hard to evaluate coaching performance accurately.
  • Predictive metrics and modeling play a crucial role in soccer analytics, especially in predicting the career progression of young players.
  • Improving tracking data and expanding its availability will be a significant focus in the future of soccer analytics.

Chapters:

00:00 Introduction to Ravi and His Role at Seattle Sounders 

06:30 Building an Analytics Department

15:00 The Impact of Analytics on Player Recruitment and Performance 

28:00 Challenges and Innovations in Soccer Analytics 

42:00 Player Health, Injury Prevention, and Training 

55:00 The Evolution of Data-Driven Strategies

01:10:00 Future of Analytics in Sports

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,

#115 Using Time Series to Estimate Uncertainty, with Nate Haines17 Sep 202401:39:51

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Takeaways:

  • State space models and traditional time series models are well-suited to forecast loss ratios in the insurance industry, although actuaries have been slow to adopt modern statistical methods.
  • Working with limited data is a challenge, but informed priors and hierarchical models can help improve the modeling process.
  • Bayesian model stacking allows for blending together different model predictions and taking the best of both (or all if more than 2 models) worlds.
  • Model comparison is done using out-of-sample performance metrics, such as the expected log point-wise predictive density (ELPD). Brute leave-future-out cross-validation is often used due to the time-series nature of the data.
  • Stacking or averaging models are trained on out-of-sample performance metrics to determine the weights for blending the predictions. Model stacking can be a powerful approach for combining predictions from candidate models. Hierarchical stacking in particular is useful when weights are assumed to vary according to covariates.
  • BayesBlend is a Python package developed by Ledger Investing that simplifies the implementation of stacking models, including pseudo Bayesian model averaging, stacking, and hierarchical stacking.
  • Evaluating the performance of patient time series models requires considering multiple metrics, including log likelihood-based metrics like ELPD, as well as more absolute metrics like RMSE and mean absolute error.
  • Using robust variants of metrics like ELPD can help address issues with extreme outliers. For example, t-distribution estimators of ELPD as opposed to sample sum/mean estimators.
  • It is important to evaluate model performance from different perspectives and consider the trade-offs between different metrics. Evaluating models based solely on traditional metrics can limit understanding and trust in the model. Consider additional factors such as interpretability, maintainability, and productionization.
  • Simulation-based calibration (SBC) is a valuable tool for assessing parameter estimation and model correctness. It allows for the interpretation of model parameters and the identification of coding errors.
  • In industries like insurance, where regulations may restrict model choices, classical statistical approaches still play a significant role. However, there is potential for Bayesian methods and generative AI in certain areas.

#114 From the Field to the Lab – A Journey in Baseball Science, with Jacob Buffa05 Sep 202401:01:32

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

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Takeaways:

  • Education and visual communication are key in helping athletes understand the impact of nutrition on performance.
  • Bayesian statistics are used to analyze player performance and injury risk.
  • Integrating diverse data sources is a challenge but can provide valuable insights.
  • Understanding the specific needs and characteristics of athletes is crucial in conditioning and injury prevention. The application of Bayesian statistics in baseball science requires experts in Bayesian methods.
  • Traditional statistical methods taught in sports science programs are limited.
  • Communicating complex statistical concepts, such as Bayesian analysis, to coaches and players is crucial.
  • Conveying uncertainties and limitations of the models is essential for effective utilization.
  • Emerging trends in baseball science include the use of biomechanical information and computer vision algorithms.
  • Improving player performance and injury prevention are key goals for the future of baseball science.

Chapters:

00:00 The Role of Nutrition and Conditioning

05:46 Analyzing Player Performance and Managing Injury Risks

12:13 Educating Athletes on Dietary Choices

18:02 Emerging Trends in Baseball Science

29:49 Hierarchical Models and Player Analysis

36:03 Challenges of Working with Limited Data

39:49 Effective Communication of Statistical Concepts

47:59 Future Trends: Biomechanical Data Analysis and Computer Vision Algorithms

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,...

#113 A Deep Dive into Bayesian Stats, with Alex Andorra, ft. the Super Data Science Podcast22 Aug 202401:30:51

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

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Takeaways:

  • Bayesian statistics is a powerful framework for handling complex problems, making use of prior knowledge, and excelling with limited data.
  • Bayesian statistics provides a framework for updating beliefs and making predictions based on prior knowledge and observed data.
  • Bayesian methods allow for the explicit incorporation of prior assumptions, which can provide structure and improve the reliability of the analysis.
  • There are several Bayesian frameworks available, such as PyMC, Stan, and Bambi, each with its own strengths and features.
  • PyMC is a powerful library for Bayesian modeling that allows for flexible and efficient computation.
  • For beginners, it is recommended to start with introductory courses or resources that provide a step-by-step approach to learning Bayesian statistics.
  • PyTensor leverages GPU acceleration and complex graph optimizations to improve the performance and scalability of Bayesian models.
  • ArviZ is a library for post-modeling workflows in Bayesian statistics, providing tools for model diagnostics and result visualization.
  • Gaussian processes are versatile non-parametric models that can be used for spatial and temporal data analysis in Bayesian statistics.

Chapters:

00:00 Introduction to Bayesian Statistics

07:32 Advantages of Bayesian Methods

16:22 Incorporating Priors in Models

23:26 Modeling Causal Relationships

30:03 Introduction to PyMC, Stan, and Bambi

34:30 Choosing the Right Bayesian Framework

39:20 Getting Started with Bayesian Statistics

44:39 Understanding Bayesian Statistics and PyMC

49:01 Leveraging PyTensor for Improved Performance and Scalability

01:02:37 Exploring Post-Modeling Workflows with ArviZ

01:08:30 The Power of Gaussian Processes in Bayesian 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 Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna,...

#112 Advanced Bayesian Regression, with Tomi Capretto07 Aug 202401:27:19

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

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Takeaways:

  • Teaching Bayesian Concepts Using M&Ms: Tomi Capretto uses an engaging classroom exercise involving M&Ms to teach Bayesian statistics, making abstract concepts tangible and intuitive for students.
  • Practical Applications of Bayesian Methods: Discussion on the real-world application of Bayesian methods in projects at PyMC Labs and in university settings, emphasizing the practical impact and accessibility of Bayesian statistics.
  • Contributions to Open-Source Software: Tomi’s involvement in developing Bambi and other open-source tools demonstrates the importance of community contributions to advancing statistical software.
  • Challenges in Statistical Education: Tomi talks about the challenges and rewards of teaching complex statistical concepts to students who are accustomed to frequentist approaches, highlighting the shift to thinking probabilistically in Bayesian frameworks.
  • Future of Bayesian Tools: The discussion also touches on the future enhancements for Bambi and PyMC, aiming to make these tools more robust and user-friendly for a wider audience, including those who are not professional statisticians. 

Chapters:

05:36 Tomi's Work and Teaching

10:28 Teaching Complex Statistical Concepts with Practical Exercises

23:17 Making Bayesian Modeling Accessible in Python

38:46 Advanced Regression with Bambi

41:14 The Power of Linear Regression

42:45 Exploring Advanced Regression Techniques

44:11 Regression Models and Dot Products

45:37 Advanced Concepts in Regression

46:36 Diagnosing and Handling Overdispersion

47:35 Parameter Identifiability and Overparameterization

50:29 Visualizations and Course Highlights

51:30 Exploring Niche and Advanced Concepts

56:56 The Power of Zero-Sum Normal

59:59 The Value of Exercises and Community

01:01:56 Optimizing Computation with Sparse Matrices

01:13:37 Avoiding MCMC and Exploring Alternatives

01:18:27 Making Connections Between Different Models

Thank you to my Patrons for making this episode...

#111 Nerdinsights from the Football Field, with Patrick Ward24 Jul 202401:25:43

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Takeaways:

  • Communicating Bayesian concepts to non-technical audiences in sports analytics can be challenging, but it is important to provide clear explanations and address limitations.
  • Understanding the model and its assumptions is crucial for effective communication and decision-making.
  • Involving domain experts, such as scouts and coaches, can provide valuable insights and improve the model's relevance and usefulness.
  • Customizing the model to align with the specific needs and questions of the stakeholders is essential for successful implementation. 
  • Understanding the needs of decision-makers is crucial for effectively communicating and utilizing models in sports analytics.
  • Predicting the impact of training loads on athletes' well-being and performance is a challenging frontier in sports analytics.
  • Identifying discrete events in team sports data is essential for analysis and development of models.

Chapters:

00:00 Bayesian Statistics in Sports Analytics

18:29 Applying Bayesian Stats in Analyzing Player Performance and Injury Risk

36:21 Challenges in Communicating Bayesian Concepts to Non-Statistical Decision-Makers

41:04 Understanding Model Behavior and Validation through Simulations

43:09 Applying Bayesian Methods in Sports Analytics

48:03 Clarifying Questions and Utilizing Frameworks

53:41 Effective Communication of Statistical Concepts

57:50 Integrating Domain Expertise with Statistical Models

01:13:43 The Importance of Good Data

01:18:11 The Future of Sports Analytics

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...

#110 Unpacking Bayesian Methods in AI with Sam Duffield10 Jul 202401:12:27

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

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Takeaways:

  • Use mini-batch methods to efficiently process large datasets within Bayesian frameworks in enterprise AI applications.
  • Apply approximate inference techniques, like stochastic gradient MCMC and Laplace approximation, to optimize Bayesian analysis in practical settings.
  • Explore thermodynamic computing to significantly speed up Bayesian computations, enhancing model efficiency and scalability.
  • Leverage the Posteriors python package for flexible and integrated Bayesian analysis in modern machine learning workflows.
  • Overcome challenges in Bayesian inference by simplifying complex concepts for non-expert audiences, ensuring the practical application of statistical models.
  • Address the intricacies of model assumptions and communicate effectively to non-technical stakeholders to enhance decision-making processes.

Chapters:

00:00 Introduction to Large-Scale Machine Learning

11:26 Scalable and Flexible Bayesian Inference with Posteriors

25:56 The Role of Temperature in Bayesian Models

32:30 Stochastic Gradient MCMC for Large Datasets

36:12 Introducing Posteriors: Bayesian Inference in Machine Learning

41:22 Uncertainty Quantification and Improved Predictions

52:05 Supporting New Algorithms and Arbitrary Likelihoods

59:16 Thermodynamic Computing

01:06:22 Decoupling Model Specification, Data Generation, and Inference

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

#109 Prior Sensitivity Analysis, Overfitting & Model Selection, with Sonja Winter25 Jun 202401:10:50

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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 methods align better with researchers' intuitive understanding of research questions and provide more tools to evaluate and understand models.
  • Prior sensitivity analysis is crucial for understanding the robustness of findings to changes in priors and helps in contextualizing research findings.
  • Bayesian methods offer an elegant and efficient way to handle missing data in longitudinal studies, providing more flexibility and information for researchers.
  • Fit indices in Bayesian model selection are effective in detecting underfitting but may struggle to detect overfitting, highlighting the need for caution in model complexity.
  • Bayesian methods have the potential to revolutionize educational research by addressing the challenges of small samples, complex nesting structures, and longitudinal data. 
  • Posterior predictive checks are valuable for model evaluation and selection.

Chapters

00:00 The Power and Importance of Priors

09:29 Updating Beliefs and Choosing Reasonable Priors

16:08 Assessing Robustness with Prior Sensitivity Analysis

34:53 Aligning Bayesian Methods with Researchers' Thinking

37:10 Detecting Overfitting in SEM

43:48 Evaluating Model Fit with Posterior Predictive Checks

47:44 Teaching Bayesian Methods

54:07 Future Developments in Bayesian Statistics

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...

#108 Modeling Sports & Extracting Player Values, with Paul Sabin14 Jun 202401:18:04

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

  • Convincing non-stats stakeholders in sports analytics can be challenging, but building trust and confirming their prior beliefs can help in gaining acceptance.
  • Combining subjective beliefs with objective data in Bayesian analysis leads to more accurate forecasts.
  • The availability of massive data sets has revolutionized sports analytics, allowing for more complex and accurate models.
  • Sports analytics models should consider factors like rest, travel, and altitude to capture the full picture of team performance.
  • The impact of budget on team performance in American sports and the use of plus-minus models in basketball and American football are important considerations in sports analytics.
  • The future of sports analytics lies in making analysis more accessible and digestible for everyday fans.
  • There is a need for more focus on estimating distributions and variance around estimates in sports analytics.
  • AI tools can empower analysts to do their own analysis and make better decisions, but it's important to ensure they understand the assumptions and structure of the data.
  • Measuring the value of certain positions, such as midfielders in soccer, is a challenging problem in sports analytics.
  • Game theory plays a significant role in sports strategies, and optimal strategies can change over time as the game evolves.

Chapters

00:00 Introduction and Overview

09:27 The Power of Bayesian Analysis in Sports Modeling

16:28 The Revolution of Massive Data Sets in Sports Analytics

31:03 The Impact of Budget in Sports Analytics

39:35 Introduction to Sports Analytics

52:22 Plus-Minus Models in American Football

01:04:11 The Future of Sports Analytics

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...

#107 Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt29 May 202401:21:37

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!


In this episode, Marvin Schmitt introduces the concept of amortized Bayesian inference, where the upfront training phase of a neural network is followed by fast posterior inference.

Marvin will guide us through this new concept, discussing his work in probabilistic machine learning and uncertainty quantification, using Bayesian inference with deep neural networks. 

He also introduces BayesFlow, a Python library for amortized Bayesian workflows, and discusses its use cases in various fields, while also touching on the concept of deep fusion and its relation to multimodal simulation-based inference.

A PhD student in computer science at the University of Stuttgart, Marvin is supervised by two LBS guests you surely know — Paul Bürkner and Aki Vehtari. Marvin’s research combines deep learning and statistics, to make Bayesian inference fast and trustworthy. 

In his free time, Marvin enjoys board games and is a passionate guitar player.

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

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, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, 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 and Blake Walters.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Takeaways:

  • Amortized Bayesian inference...
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