Episoder
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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:
Teemu focuses on calibration assessments and predictive checking in Bayesian workflows.Simulation-based calibration (SBC) checks model implementationSBC 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...
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ICYMI, I'll be in London next week, for a live episode of the Learning Bayesian Statistics podcast 🍾
Come say hi on June 24 at Imperial College London! We'll be talking about uncertainty quantification — not just in theory, but in the messy, practical reality of building models that are supposed to work in the real world.
🎟️ Get your tickets!
Some of the questions we’ll unpack:
🔍 Why is it so hard to model uncertainty reliably?
⚠️ How do overconfident models break things in production?
🧠 What tools and frameworks help today?
🔄 What do we need to rethink if we want robust ML over the next decade?
Joining me on stage: the brilliant Mélodie Monod, Yingzhen Li and François-Xavier Briol -- researchers doing cutting-edge work on these questions, across Bayesian methods, statistical learning, and real-world ML deployment.
A huge thank you to Oliver Ratmann for setting this up!
📍 Imperial-X, White City Campus (Room LRT 608)
🗓️ June 24, 11:30–13:00
🎙️ Doors open at 11:30 — we start at noon sharp
Come say hi, ask hard questions, and be part of the recording.
🎟️ Get your tickets!
Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh,...
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Mangler du episoder?
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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.
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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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:
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...
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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.
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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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:
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...
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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.
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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Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
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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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.
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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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 ;)
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. -
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.
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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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 ;)
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... -
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
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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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:
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 methodsCollaboration 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,...
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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:
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
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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 ;)
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... -
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:
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,...
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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 ;)
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... -
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
My Intuitive Bayes Online Courses1:1 Mentorship with meOur 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 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
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Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
My Intuitive Bayes Online Courses1:1 Mentorship with meOur 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 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...
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