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224 afleveringen, van nieuw naar oud
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BITESIZE | Are Bayesian Models the Missing Ingredient in Nutrition Research?
23 oktober 2025 · 23 min
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Soccer Factor Model Dashboard
Today’s clip is from episode 143 of the podcast, with Christoph Bamberg.
Christoph shares his journey into Bayesian statistics and computational modeling, the challenges faced in academia, and the technical tools used in research.
Alex and Christoph delve into a specific study on appetite regulation and cognitive performance, exploring the implications of framing in psychological research and the importance of careful communication in health-related contexts.
Get the full discussion here.
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
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Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#143 Transforming Nutrition Science with Bayesian Methods, with Christoph Bamberg
15 oktober 2025 · 1 uur 13 min
Sign up for Alex's first live cohort, about Hierarchical Model building! Lees meer
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
Bayesian mindset in psychology: Why priors, model checking, and full uncertainty reporting make findings more honest and useful.
Intermittent fasting & cognition: A Bayesian meta-analysis suggests effects are context- and age-dependent – and often small but meaningful.
Framing matters: The way we frame dietary advice (focus, flexibility, timing) can shape adherence and perceived cognitive benefits.
From cravings to choices: Appetite, craving, stress, and mood interact to influence eating and cognitive performance throughout the day.
Define before you measure: Clear definitions (and DAGs to encode assumptions) reduce ambiguity and guide better study design.
DAGs for causal thinking: Directed acyclic graphs help separate hypotheses from data pipelines and make causal claims auditable.
Small effects, big implications: Well-estimated “small” effects can scale to public-health relevance when decisions repeat daily.
Teaching by modeling: Helping students write models (not just run them) builds statistical thinking and scientific literacy.
Bridging lab and life: Balancing careful experiments with real-world measurement is key to actionable health-psychology insights.
Trust through transparency: Openly communicating assumptions, uncertainty, and limitations strengthens scientific credibility.
Chapters:
10:35 The Struggles of Bayesian Statistics in Psychology
22:30 Exploring Appetite and Cognitive Performance
29:45 Research Methodology and Causal Inference
36:36 Understanding Cravings and Definitions
39:02 Intermittent Fa…
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BITESIZE | How Bayesian Additive Regression Trees Work in Practice
9 oktober 2025 · 23 min
Soccer Factor Model Dashboard Lees meer
Unveiling True Talent: The Soccer Factor Model for Skill Evaluation
LBS #91, Exploring European Football Analytics, with Max Göbel
Get early access to Alex's next live-cohort courses!
Today’s clip is from episode 142 of the podcast, with Gabriel Stechschulte.
Alex and Garbriel explore the re-implementation of BART (Bayesian Additive Regression Trees) in Rust, detailing the technical challenges and performance improvements achieved.
They also share insights into the benefits of BART, such as uncertainty quantification, and its application in various data-intensive fields.
Get the full discussion here.
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte
2 oktober 2025 · 1 uur 10 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Get early access to Alex's next live-cohort courses!
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
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,…
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BITESIZE | How Probability Becomes Causality?
24 september 2025 · 22 min
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Today’s clip is from episode 141 of the podcast, with Sam Witty.
Alex and Sam discuss the ChiRho project, delving into the intricacies of causal inference, particularly focusing on Do-Calculus, regression discontinuity designs, and Bayesian structural causal inference.
They explain ChiRho's design philosophy, emphasizing its modular and extensible nature, and highlights the importance of efficient estimation in causal inference, making complex statistical methods accessible to users without extensive expertise.
Get the full discussion here.
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#141 AI Assisted Causal Inference, with Sam Witty
18 september 2025 · 1 uur 38 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Get early access to Alex's next live-cohort courses!
Enroll in the Causal AI workshop, to learn live with Alex (15% off if you're a Patron of the show)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
Causal inference is crucial for understanding the impact of interventions in various fields.
ChiRho is a causal probabilistic programming language that bridges mechanistic and data-driven models.
ChiRho allows for easy manipulation of causal models and counterfactual reasoning.
The design of ChiRho emphasizes modularity and extensibility for diverse applications.
Causal inference requires careful consideration of assumptions and model structures.
Real-world applications of causal inference can lead to significant insights in science and engineering.
Collaboration and communication are key in translating causal questions into actionable models.
The future of causal inference lies in integrating probabilistic programming with scientific discovery.
Chapters:
05:53 Bridging Mechanistic and Data-Driven Models
09:13 Understanding Causal Probabilistic Programming
12:10 ChiRho and Its Design Principles
15:03 ChiRho’s Functionality and Use Cases
17:55 Counterfactual Worlds and Mediation Analysis
20:47 Efficient Estimation in ChiRho
24:08 Future Directions for Causal AI
50:21 Understanding the Do-Operator in Causal Inference
56:45 ChiRho’s Role in Causal Inference and Bayesian Modeling
01:01:36 Roadmap and Future Developments for ChiRho
01:05:29 Real-World Applications of Causal Probabilistic Programming
01:10:51 Challenges in Causal Inference Adoption
01:11:50 The Importance of Causal Claims in Research
01:18:11 Bayesian Approaches to Causal Inference
01:22:08 Combining Gaussian Processes with Causal Inference
01:28:27 Future Dir…
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BITESIZE | How to Think Causally About Your Models?
10 september 2025 · 24 min
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Today’s clip is from episode 140 of the podcast, with Ron Yurko.
Alex and Ron discuss the challenges of model deployment, and the complexities of modeling player contributions in team sports like soccer and football.
They emphasize the importance of understanding replacement levels, the Going Deep framework in football analytics, and the need for proper modeling of expected points.
Additionally, they share insights on teaching Bayesian modeling to students and the difficulties they face in grasping the concepts of model writing and application.
Get the full discussion here.
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#140 NFL Analytics & Teaching Bayesian Stats, with Ron Yurko
3 september 2025 · 1 uur 33 min
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Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
Teaching students to write out their own models is crucial.
Developing a sports analytics portfolio is essential for aspiring analysts.
Modeling expectations in sports analytics can be misleading.
Tracking data can significantly improve player performance models.
Ron encourages students to engage in active learning through projects.
The importance of understanding the dependency structure in data is vital.
Ron aims to integrate more diverse sports analytics topics into his teaching.
Chapters:
03:51 The Journey into Sports Analytics
15:20 The Evolution of Bayesian Statistics in Sports
26:01 Innovations in NFL WAR Modeling
39:23 Causal Modeling in Sports Analytics
46:29 Defining Replacement Levels in Sports
48:26 The Going Deep Framework and Big Data in Football
52:47 Modeling Expectations in Football Data
55:40 Teaching Statistical Concepts in Sports Analytics
01:01:54 The Importance of Model Building in Education
01:04:46 Statistical Thinking in Sports Analytics
01:10:55 Innovative Research in Player Movement
01:15:47 Exploring Data Needs in American Football
01:18:43 Building a Sports Analytics Portfolio
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Mic…
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BITESIZE | Is Bayesian Optimization the Answer?
27 augustus 2025 · 25 min
Today’s clip is from episode 139 of the podcast, with with Max Balandat. Lees meer
Alex and Max discuss the integration of BoTorch with PyTorch, exploring its applications in Bayesian optimization and Gaussian processes. They highlight the advantages of using GPyTorch for structured matrices and the flexibility it offers for research.
The discussion also covers the motivations behind building BoTorch, the importance of open-source culture at Meta, and the role of PyTorch in modern machine learning.
Get the full discussion here.
Attend Alex's tutorial at PyData Berlin: A Beginner's Guide to State Space Modeling
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#139 Efficient Bayesian Optimization in PyTorch, with Max Balandat
20 augustus 2025 · 1 uur 25 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
BoTorch is designed for researchers who want flexibility in Bayesian optimization.
The integration of BoTorch with PyTorch allows for differentiable programming.
Scalability at Meta involves careful software engineering practices and testing.
Open-source contributions enhance the development and community engagement of BoTorch.
LLMs can help incorporate human knowledge into optimization processes.
Max emphasizes the importance of clear communication of uncertainty to stakeholders.
The role of a researcher in industry is often more application-focused than in academia.
Max's team at Meta works on adaptive experimentation and Bayesian optimization.
Chapters:
08:51 Understanding BoTorch
12:12 Use Cases and Flexibility of BoTorch
15:02 Integration with PyTorch and GPyTorch
17:57 Practical Applications of BoTorch
20:50 Open Source Culture at Meta and BoTorch's Development
43:10 The Power of Open Source Collaboration
47:49 Scalability Challenges at Meta
51:02 Balancing Depth and Breadth in Problem Solving
55:08 Communicating Uncertainty to Stakeholders
01:00:53 Learning from Missteps in Research
01:05:06 Integrating External Contributions into BoTorch
01:08:00 The Future of Optimization with LLMs
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Coli…
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BITESIZE | What's Missing in Bayesian Deep Learning?
13 augustus 2025 · 21 min
Today’s clip is from episode 138 of the podcast, with Mélodie Monod, François-Xavier Briol and Yingzhen Li. Lees meer
During this live show at Imperial College London, Alex and his guests delve into the complexities and advancements in Bayesian deep learning, focusing on uncertainty quantification, the integration of machine learning tools, and the challenges faced in simulation-based inference.
The speakers discuss their current projects, the evolution of Bayesian models, and the need for better computational tools in the field.
Get the full discussion here.
Attend Alex's tutorial at PyData Berlin: A Beginner's Guide to State Space Modeling
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#138 Quantifying Uncertainty in Bayesian Deep Learning, Live from Imperial College London
6 augustus 2025 · 1 uur 23 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
Bayesian deep learning is a growing field with many challenges.
Current research focuses on applying Bayesian methods to neural networks.
Diffusion methods are emerging as a new approach for uncertainty quantification.
The integration of machine learning tools into Bayesian models is a key area of research.
The complexity of Bayesian neural networks poses significant computational challenges.
Future research will focus on improving methods for uncertainty quantification. Generalized Bayesian inference offers a more robust approach to uncertainty.
Uncertainty quantification is crucial in fields like medicine and epidemiology.
Detecting out-of-distribution examples is essential for model reliability.
Exploration-exploitation trade-off is vital in reinforcement learning.
Marginal likelihood can be misleading for model selection.
The integration of Bayesian methods in LLMs presents unique challenges.
Chapters:
00:00 Introduction to Bayesian Deep Learning
03:12 Panelist Introductions and Backgrounds
10:37 Current Research and Challenges in Bayesian Deep Learning
18:04 Contrasting Approaches: Bayesian vs. Machine Learning
26:09 Tools and Techniques for Bayesian Deep Learning
31:18 Innovative Methods in Uncertainty Quantification
36:23 Generalized Bayesian Inference and Its Implications
41:38 Robust Bayesian Inference and Gaussian Processes
44:24 Software Development in Bayesian Statistics
46:51 Understanding Uncertainty in Language Models
50:03 Hallucinations in Language Models
53:48 Bayesian Neural Networks vs Traditional Neural Networks
58:00 Challenges with Likelihood Assumptions…
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BITESIZE | Practical Applications of Causal AI with LLMs, with Robert Ness
30 juli 2025 · 25 min
Today’s clip is from episode 137 of the podcast, with Robert Ness. Lees meer
Alex and Robert discuss the intersection of causal inference and deep learning, emphasizing the importance of understanding causal concepts in statistical modeling.
The discussion also covers the evolution of probabilistic machine learning, the role of inductive biases, and the potential of large language models in causal analysis, highlighting their ability to translate natural language into formal causal queries.
Get the full conversation here.
Attend Alex's tutorial at PyData Berlin: A Beginner's Guide to State Space Modeling
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#137 Causal AI & Generative Models, with Robert Ness
23 juli 2025 · 1 uur 38 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
Causal assumptions are crucial for statistical modeling.
Deep learning can be integrated with causal models.
Statistical rigor is essential in evaluating LLMs.
Causal representation learning is a growing field.
Inductive biases in AI should match key mechanisms.
Causal AI can improve decision-making processes.
The future of AI lies in understanding causal relationships.
Chapters:
00:00 Introduction to Causal AI and Its Importance
16:34 The Journey to Writing Causal AI
28:05 Integrating Graphical Causality with Deep Learning
40:10 The Evolution of Probabilistic Machine Learning
44:34 Practical Applications of Causal AI with LLMs
49:48 Exploring Multimodal Models and Causality
56:15 Tools and Frameworks for Causal AI
01:03:19 Statistical Rigor in Evaluating LLMs
01:12:22 Causal Thinking in Real-World Deployments
01:19:52 Trade-offs in Generative Causal Models
01:25:14 Future of Causal Generative Modeling
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labo…
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BITESIZE | How to Make Your Models Faster, with Haavard Rue & Janet van Niekerk
16 juli 2025 · 18 min
Today’s clip is from episode 136 of the podcast, with Haavard Rue & Janet van Niekerk. Lees meer
Alex, Haavard and Janet explore the world of Bayesian inference with INLA, a fast and deterministic method that revolutionizes how we handle large datasets and complex models.
Discover the power of INLA, and why it can make your models go much faster! Get the full conversation here.
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#136 Bayesian Inference at Scale: Unveiling INLA, with Haavard Rue & Janet van Niekerk
9 juli 2025 · 1 uur 18 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
INLA is a fast, deterministic method for Bayesian inference.
INLA is particularly useful for large datasets and complex models.
The R INLA package is widely used for implementing INLA methodology.
INLA has been applied in various fields, including epidemiology and air quality control.
Computational challenges in INLA are minimal compared to MCMC methods.
The Smart Gradient method enhances the efficiency of INLA.
INLA can handle various likelihoods, not just Gaussian.
SPDs allow for more efficient computations in spatial modeling.
The new INLA methodology scales better for large datasets, especially in medical imaging.
Priors in Bayesian models can significantly impact the results and should be chosen carefully.
Penalized complexity priors (PC priors) help prevent overfitting in models.
Understanding the underlying mathematics of priors is crucial for effective modeling.
The integration of GPUs in computational methods is a key future direction for INLA.
The development of new sparse solvers is essential for handling larger models efficiently.
Chapters:
06:06 Understanding INLA: A Comparison with MCMC
08:46 Applications of INLA in Real-World Scenarios
11:58 Latent Gaussian Models and Their Importance
15:12 Impactful Applications of INLA in Health and Environment
18:09 Computational Challenges and Solutions in INLA
21:06 Stochastic Partial Differential Equations in Spatial Modeling
23:55 Future Directions and Innovations in INLA
39:51 Exploring Stochastic Differential Equations
43:02 Advancements in INLA Methodology
50:40 Getting Started with INLA
56:25 Understanding Priors in …
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BITESIZE | Understanding Simulation-Based Calibration, with Teemu Säilynoja
4 juli 2025 · 21 min
Get 10% off Hugo's "Building LLM Applications for Data Scientists and Software Engineers" online course! Lees meer
Today’s clip is from episode 135 of the podcast, with Teemu Säilynoja.
Alex and Teemu discuss the importance of simulation-based calibration (SBC). They explore the practical implementation of SBC in probabilistic programming languages, the challenges faced in developing SBC methods, and the significance of both prior and posterior SBC in ensuring model reliability.
The discussion emphasizes the need for careful model implementation and inference algorithms to achieve accurate calibration.
Get the full conversation here.
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#135 Bayesian Calibration and Model Checking, with Teemu Säilynoja
25 juni 2025 · 1 uur 12 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
Teemu focuses on calibration assessments and predictive checking in Bayesian workflows.
Simulation-based calibration (SBC) checks model implementation
SBC involves drawing realizations from prior and generating prior predictive data.
Visual predictive checking is crucial for assessing model predictions.
Prior predictive checks should be done before looking at data.
Posterior SBC focuses on the area of parameter space most relevant to the data.
Challenges in SBC include inference time.
Visualizations complement numerical metrics in Bayesian modeling.
Amortized Bayesian inference benefits from SBC for quick posterior checks. The calibration of Bayesian models is more intuitive than Frequentist models.
Choosing the right visualization depends on data characteristics.
Using multiple visualization methods can reveal different insights.
Visualizations should be viewed as models of the data.
Goodness of fit tests can enhance visualization accuracy.
Uncertainty visualization is crucial but often overlooked.
Chapters:
09:53 Understanding Simulation-Based Calibration (SBC)
15:03 Practical Applications of SBC in Bayesian Modeling
22:19 Challenges in Developing Posterior SBC
29:41 The Role of SBC in Amortized Bayesian Inference
33:47 The Importance of Visual Predictive Checking
36:50 Predictive Checking and Model Fitting
38:08 The Importance of Visual Checks
40:54 Choosing Visualization Types
49:06 Visualizations as Models
55:02 Uncertainty Visualization in Bayesian Modeling
01:00:05 Future Trends in Probabilistic Modeling
Thank you to my Patrons for making this episode possible!
Yusuk…
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Live Show Announcement | Come Meet Me in London!
19 juni 2025 · 3 min
ICYMI, I'll be in London next week, for a live episode of the Learning Bayesian Statistics podcast 🍾 Lees meer
Come say hi on June 24 at Imperial College London! We'll be talking about uncertainty quantification — not just in theory, but in the messy, practical reality of building models that are supposed to work in the real world.
🎟️ Get your tickets!
Some of the questions we’ll unpack:
🔍 Why is it so hard to model uncertainty reliably?
⚠️ How do overconfident models break things in production?
🧠 What tools and frameworks help today?
🔄 What do we need to rethink if we want robust ML over the next decade?
Joining me on stage: the brilliant Mélodie Monod, Yingzhen Li and François-Xavier Briol -- researchers doing cutting-edge work on these questions, across Bayesian methods, statistical learning, and real-world ML deployment.
A huge thank you to Oliver Ratmann for setting this up!
📍 Imperial-X, White City Campus (Room LRT 608)
🗓️ June 24, 11:30–13:00
🎙️ Doors open at 11:30 — we start at noon sharp
Come say hi, ask hard questions, and be part of the recording.
🎟️ Get your tickets!
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell…
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BITESIZE | Exploring Dynamic Regression Models, with David Kohns
18 juni 2025 · 15 min
Today’s clip is from episode 134 of the podcast, with David Kohns. Lees meer
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.
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#134 Bayesian Econometrics, State Space Models & Dynamic Regression, with David Kohns
10 juni 2025 · 1 uur 41 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
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 Ostheg…
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BITESIZE | Why Your Models Might Be Wrong & How to Fix it, with Sean Pinkney & Adrian Seyboldt
4 juni 2025 · 17 min
Today’s clip is from episode 133 of the podcast, with Sean Pinkney & Adrian Seyboldt. Lees meer
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.
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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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Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#133 Making Models More Efficient & Flexible, with Sean Pinkney & Adrian Seyboldt
28 mei 2025 · 1 uur 12 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
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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, Bradle…
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BITESIZE | How AI is Redefining Human Interactions, with Tom Griffiths
21 mei 2025 · 22 min
Today’s clip is from episode 132 of the podcast, with Tom Griffiths. Lees meer
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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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
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#132 Bayesian Cognition and the Future of Human-AI Interaction, with Tom Griffiths
13 mei 2025 · 1 uur 30 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Check out Hugo’s latest episode with Fei-Fei Li, on How Human-Centered AI Actually Gets Built
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
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
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BITESIZE | Hacking Bayesian Models for Better Performance, with Luke Bornn
7 mei 2025 · 14 min
Today’s clip is from episode 131 of the podcast, with Luke Bornn. Lees meer
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.
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
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Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#131 Decision-Making Under High Uncertainty, with Luke Bornn
30 april 2025 · 1 uur 32 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, 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, Davi…
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BITESIZE | Real-World Applications of Models in Public Health, with Adam Kucharski
23 april 2025 · 16 min
Today’s clip is from episode 130 of the podcast, with epidemiological modeler Adam Kucharski. Lees meer
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.
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
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Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#130 The Real-World Impact of Epidemiological Models, with Adam Kucharski
16 april 2025 · 1 uur 9 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, 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, Davi…
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BITESIZE | The Why & How of Bayesian Deep Learning, with Vincent Fortuin
9 april 2025 · 12 min
Today’s clip is from episode 129 of the podcast, with AI expert and researcher Vincent Fortuin. Lees meer
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
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
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Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#129 Bayesian Deep Learning & AI for Science with Vincent Fortuin
2 april 2025 · 1 uur 3 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
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 …
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#128 Building a Winning Data Team in Football, with Matt Penn
19 maart 2025 · 58 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
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 Analys…
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#127 Saving Sharks... with Python, Causal Inference and Aaron MacNeil
5 maart 2025 · 1 uur 4 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, 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, Davi…
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#126 MMM, CLV & Bayesian Marketing Analytics, with Will Dean
19 februari 2025 · 55 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
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, Pat…
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#125 Bayesian Sports Analytics & The Future of PyMC, with Chris Fonnesbeck
5 februari 2025 · 58 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, 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.
…
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#124 State Space Models & Structural Time Series, with Jesse Grabowski
22 januari 2025 · 1 uur 36 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
My Intuitive Bayes Online Courses
1:1 Mentorship with me
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 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 Je…
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#123 BART & The Future of Bayesian Tools, with Osvaldo Martin
10 januari 2025 · 1 uur 32 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
My Intuitive Bayes Online Courses
1:1 Mentorship with me
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 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 Salv…
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#122 Learning and Teaching in the Age of AI, with Hugo Bowne-Anderson
26 december 2024 · 1 uur 23 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
My Intuitive Bayes Online Courses
1:1 Mentorship with me
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:
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 Envis…
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#121 Exploring Bayesian Structural Equation Modeling, with Nathaniel Forde
11 december 2024 · 1 uur 8 min
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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 Di…
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#120 Innovations in Infectious Disease Modeling, with Liza Semenova & Chris Wymant
27 november 2024 · 1 uur 2 min
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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:
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...
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#119 Causal Inference, Fiction Writing and Career Changes, with Robert Kubinec
13 november 2024 · 1 uur 25 min
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1:1 Mentorship with me
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:
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 …
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#118 Exploring the Future of Stan, with Charles Margossian & Brian Ward
30 oktober 2024 · 59 min
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My Intuitive Bayes Online Courses
1:1 Mentorship with me
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:
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,…
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#117 Unveiling the Power of Bayesian Experimental Design, with Desi Ivanova
15 oktober 2024 · 1 uur 13 min
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My Intuitive Bayes Online Courses
1:1 Mentorship with me
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:
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 Maey…
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#116 Mastering Soccer Analytics, with Ravi Ramineni
2 oktober 2024 · 1 uur 33 min
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My Intuitive Bayes Online Courses
1:1 Mentorship with me
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:
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 …
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#115 Using Time Series to Estimate Uncertainty, with Nate Haines
17 september 2024 · 1 uur 40 min
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My Intuitive Bayes Online Courses
1:1 Mentorship with me
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:
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 fro…
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#114 From the Field to the Lab – A Journey in Baseball Science, with Jacob Buffa
5 september 2024 · 1 uur 2 min
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My Intuitive Bayes Online Courses
1:1 Mentorship with me
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:
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 Wi…
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#113 A Deep Dive into Bayesian Stats, with Alex Andorra, ft. the Super Data Science Podcast
22 augustus 2024 · 1 uur 31 min
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! Lees meer
My Intuitive Bayes Online Courses
1:1 Mentorship with me
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 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 Workflow…
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#112 Advanced Bayesian Regression, with Tomi Capretto
7 augustus 2024 · 1 uur 27 min
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1:1 Mentorship with me
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 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 a…
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#111 Nerdinsights from the Football Field, with Patrick Ward
24 juli 2024 · 1 uur 26 min
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My Intuitive Bayes Online Courses
1:1 Mentorship with me
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:
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 Bento…
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#110 Unpacking Bayesian Methods in AI with Sam Duffield
10 juli 2024 · 1 uur 12 min
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My Intuitive Bayes Online Courses
1:1 Mentorship with me
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:
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 Maldo…