Learning Bayesian Statistics

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

• 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 from different perspectives and consider the trade-offs between different metrics. Evaluating models based solely on traditional metrics can limit understanding and trust in the model. Consider additional factors such as interpretability, maintainability, and productionization.
• Simulation-based calibration (SBC) is a valuable tool for assessing parameter estimation and model correctness. It allows for the interpretation of model parameters and the identification of coding errors.
• In industries like insurance, where regulations may restrict model choices, classical statistical approaches still play a significant role. However, there is potential for Bayesian methods and generative AI in certain areas.

Chapters:

00:00 Introduction to Bayesian Modeling in Insurance

13:00 Time Series Models and Their Applications

30:51 Bayesian Model Averaging Explained

56:20 Impact of External Factors on Forecasting

01:25:03 Future of Bayesian Modeling and AI

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 and Francesco Madrisotti.

Links from the show:

• Nate’s website: http://haines-lab.com/
• Nate on GitHub: https://github.com/Nathaniel-Haines
• Nate on Linkedin: https://www.linkedin.com/in/nathaniel-haines-216049101/
• Nate on Twitter: https://x.com/nate__haines
• Nate on Google Scholar: https://scholar.google.com/citations?user=lg741SgAAAAJ
• LBS #14 Hidden Markov Models & Statistical Ecology, with Vianey Leos-Barajas: https://learnbayesstats.com/episode/14-hidden-markov-models-statistical-ecology-with-vianey-leos-barajas/
• LBS #107 Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt: https://learnbayesstats.com/episode/107-amortized-bayesian-inference-deep-neural-networks-marvin-schmitt/
• LBS #109 Prior Sensitivity Analysis, Overfitting & Model Selection, with Sonja Winter: https://learnbayesstats.com/episode/109-prior-sensitivity-analysis-overfitting-model-selection-sonja-winter/
• BayesBlend – Easy Model Blending: https://arxiv.org/abs/2405.00158
• BayesBlend documentation: https://ledger-investing-bayesblend.readthedocs-hosted.com/en/latest/
• SBC paper: https://arxiv.org/abs/1804.06788
• Isaac Asimov’s Foundation (Hari Seldon): https://en.wikipedia.org/wiki/Hari_Seldon
• Stancon 2023 talk on Ledger’s Bayesian modeling workflow: https://github.com/stan-dev/stancon2023/blob/main/Nathaniel-Haines/slides.pdf
• Ledger’s Bayesian modeling workflow: https://arxiv.org/abs/2407.14666v1
• More on Ledger Investing: https://www.ledgerinvesting.com/about-us

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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

• 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 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 and Francesco Madrisotti.

Links from the show:

• LBS Sports Analytics playlist: https://www.youtube.com/playlist?list=PL7RjIaSLWh5kDiPVMUSyhvFaXL3NoXOe4
• Jacob on Linkedin: https://www.linkedin.com/in/jacob-buffa-46bb7481/
• Jacob on Twitter: https://x.com/EBA_Buffa
• The Book – Playing The Percentages In Baseball: https://www.amazon.com/Book-Playing-Percentages-Baseball/dp/1494260174
• Future Value – The Battle for Baseball's Soul and How Teams Will Find the Next Superstar: https://www.amazon.com/Future-Value-Battle-Baseballs-Superstar/dp/1629377678
• The MVP Machine – How Baseball's New Nonconformists Are Using Data to Build Better Players: https://www.amazon.com/MVP-Machine-Baseballs-Nonconformists-Players/dp/1541698940

Transcript:

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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

• 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 Workflows with ArviZ

01:08:30 The Power of Gaussian Processes in Bayesian Modeling

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, 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 and Francesco Madrisotti.

Links from the show:

• Original episode on the Super Data Science podcast: https://www.superdatascience.com/podcast/bayesian-methods-and-applications-with-alexandre-andorra
• Advanced Regression with Bambi and PyMC: https://www.intuitivebayes.com/advanced-regression
• Gaussian Processes: HSGP Reference & First Steps: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Basic.html
• Modeling Webinar – Fast & Efficient Gaussian Processes: https://www.youtube.com/watch?v=9tDMouGue8g
• Modeling spatial data with Gaussian processes in PyMC: https://www.pymc-labs.com/blog-posts/spatial-gaussian-process-01/
• Hierarchical Bayesian Modeling of Survey Data with Post-stratification: https://www.pymc-labs.com/blog-posts/2022-12-08-Salk/
• PyMC docs: https://www.pymc.io/welcome.html
• Bambi docs: https://bambinos.github.io/bambi/
• PyMC Labs: https://www.pymc-labs.com/
• LBS #50 Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalter/
• LBS #51 Bernoulli’s Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://learnbayesstats.com/episode/51-bernoullis-fallacy-crisis-modern-science-aubrey-clayton/
• LBS #63 Media Mix Models & Bayes for Marketing, with Luciano Paz: https://learnbayesstats.com/episode/63-media-mix-models-bayes-marketing-luciano-paz/
• LBS #83 Multilevel Regression, Post-Stratification & Electoral Dynamics, with Tarmo Jüristo: https://learnbayesstats.com/episode/83-multilevel-regression-post-stratification-electoral-dynamics-tarmo-juristo/
• Jon Krohn on YouTube: https://www.youtube.com/JonKrohnLearns
• Jon Krohn on Linkedin: https://www.linkedin.com/in/jonkrohn/
• Jon Krohn on Twitter: https://x.com/JonKrohnLearns

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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

• 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:

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

Chapters:

05:36 Tomi's Work and Teaching

10:28 Teaching Complex Statistical Concepts with Practical Exercises

23:17 Making Bayesian Modeling Accessible in Python

38:46 Advanced Regression with Bambi

41:14 The Power of Linear Regression

42:45 Exploring Advanced Regression Techniques

44:11 Regression Models and Dot Products

45:37 Advanced Concepts in Regression

46:36 Diagnosing and Handling Overdispersion

47:35 Parameter Identifiability and Overparameterization

50:29 Visualizations and Course Highlights

51:30 Exploring Niche and Advanced Concepts

56:56 The Power of Zero-Sum Normal

59:59 The Value of Exercises and Community

01:01:56 Optimizing Computation with Sparse Matrices

01:13:37 Avoiding MCMC and Exploring Alternatives

01:18:27 Making Connections Between Different Models

Thank you to my Patrons for making this episode 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 and Francesco Madrisotti.

Links from the show:

• Tomi’s website: https://tomicapretto.com/
• Tomi on GitHub: https://github.com/tomicapretto
• Tomi on Linkedin: https://www.linkedin.com/in/tom%C3%A1s-capretto-a89873106/
• Tomi on Twitter: https://x.com/caprettotomas
• Advanced Regression course (get 10% off if you’re a Patron of the show): https://www.intuitivebayes.com/advanced-regression
• Bambi: https://bambinos.github.io/bambi/
• LBS #35 The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner/
• LBS #1 Bayes, open-source and bioinformatics, with Osvaldo Martin: https://learnbayesstats.com/episode/1-bayes-open-source-and-bioinformatics-with-osvaldo-martin/
• patsy - Describing statistical models in Python: https://patsy.readthedocs.io/en/latest/
• formulae - Formulas for mixed-models in Python: https://bambinos.github.io/formulae/
• Introducing Bayesian Analysis With m&m's®: An Active-Learning Exercise for Undergraduates: https://www.tandfonline.com/doi/full/10.1080/10691898.2019.1604106
• Richly Parameterized Linear Models Additive, Time Series, and Spatial Models Using Random Effects https://www.routledge.com/Richly-Parameterized-Linear-Models-Additive-Time-Series-and-Spatial-Models-Using-Random-Effects/Hodges/p/book/9780367533731
• Dan Simpson’s Blog (link to blogs with the ‘sparse matrices’ tag): https://dansblog.netlify.app/#category=Sparse%20matrices
• Repository for Sparse Matrix-Vector dot product: https://github.com/tomicapretto/dot_tests

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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

• 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 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 and Francesco Madrisotti.

Links from the show:

• LBS Sports Analytics playlist: https://www.youtube.com/playlist?list=PL7RjIaSLWh5kDiPVMUSyhvFaXL3NoXOe4
• Patrick’s website: http://optimumsportsperformance.com/blog/
• Patrick on GitHub: https://github.com/pw2
• Patrick on Linkedin: https://www.linkedin.com/in/patrickward02/
• Patrick on Twitter: https://twitter.com/OSPpatrick
• Patrick & Ellis Screencast: https://github.com/thebioengineer/TidyX
• Patrick on Research Gate: https://www.researchgate.net/profile/Patrick-Ward-10

Transcript:

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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

• 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 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 and Francesco Madrisotti.

Links from the show:

• Sam on Twitter: https://x.com/Sam_Duffield
• Sam on Scholar: https://scholar.google.com/citations?user=7wm_ka8AAAAJ&hl=en&oi=ao 
• Sam on Linkedin: https://www.linkedin.com/in/samduffield/
• Sam on GitHub: https://github.com/SamDuffield
• Posteriors paper (new!): https://arxiv.org/abs/2406.00104 
• Blog post introducing Posteriors: https://blog.normalcomputing.ai/posts/introducing-posteriors/posteriors.html
• Posteriors docs: https://normal-computing.github.io/posteriors/
• Paper introducing Posteriors – Scalable Bayesian Learning with posteriors: https://arxiv.org/abs/2406.00104v1
• Normal Computing scholar: https://scholar.google.com/citations?hl=en&user=jGCLWRUAAAAJ&view_op=list_works
• Thermo blogs: https://blog.normalcomputing.ai/posts/2023-11-09-thermodynamic-inversion/thermo-inversion.html
• https://blog.normalcomputing.ai/posts/thermox/thermox.html
• Great paper on SGMCMC: https://proceedings.neurips.cc/paper_files/paper/2015/file/9a4400501febb2a95e79248486a5f6d3-Paper.pdf
• David MacKay textbook on Sustainable Energy: https://www.withouthotair.com/
• LBS #107 - Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt: https://learnbayesstats.com/episode/107-amortized-bayesian-inference-deep-neural-networks-marvin-schmitt/
• LBS #98 - Fusing Statistical Physics, Machine Learning & Adaptive MCMC, with Marylou Gabrié: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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

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

Chapters

00:00 The Power and Importance of Priors

09:29 Updating Beliefs and Choosing Reasonable Priors

16:08 Assessing Robustness with Prior Sensitivity Analysis

34:53 Aligning Bayesian Methods with Researchers' Thinking

37:10 Detecting Overfitting in SEM

43:48 Evaluating Model Fit with Posterior Predictive Checks

47:44 Teaching Bayesian Methods

54:07 Future Developments in Bayesian Statistics

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi 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 and Francesco Madrisotti.

Links from the show

• Sonja’s website: https://winterstat.github.io/
• Sonja on Twitter: https://twitter.com/winterstat
• Sonja on GitHub: https://github.com/winterstat
• Under-Fitting and Over-Fitting – The Performance of Bayesian Model Selection and Fit Indices in SEM: https://www.tandfonline.com/doi/full/10.1080/10705511.2023.2280952
• LBS #102 – Bayesian Structural Equation Modeling & Causal Inference in Psychometrics, with Ed Merkle: https://youtu.be/lXd-qstzTh4?si=jLg_qZTt1oQqRO0R
• LBS #107 - Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt: https://learnbayesstats.com/episode/107-amortized-bayesian-inference-deep-neural-networks-marvin-schmitt/
• BayesFlow tutorial: https://bayesflow.org/_examples/Intro_Amortized_Posterior_Estimation.html
• LBS #106 Active Statistics, Two Truths & a Lie, with Andrew Gelman: https://learnbayesstats.com/episode/106-active-statistics-two-truths-a-lie-andrew-gelman/
• LBS #61 Why we still use non-Bayesian methods, with EJ Wagenmakers: https://learnbayesstats.com/episode/61-why-we-still-use-non-bayesian-methods-ej-wagenmakers/
• Bayesian Workflow paper: https://arxiv.org/abs/2011.01808
• Michael Betancourts'blog: https://betanalpha.github.io/writing/
• LBS #35 The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner/
• Bayesian Model-Building Interface in Python: https://bambinos.github.io/bambi/
• Advanced Regression online course: https://www.intuitivebayes.com/advanced-regression
• BLIMP: https://www.appliedmissingdata.com/blimp 

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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

• 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

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

Chapters

00:00 Introduction and Overview

09:27 The Power of Bayesian Analysis in Sports Modeling

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

31:03 The Impact of Budget in Sports Analytics

39:35 Introduction to Sports Analytics

52:22 Plus-Minus Models in American Football

01:04:11 The Future of Sports Analytics

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi 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 and Francesco Madrisotti.

Links from the show:

• LBS Sports Analytics playlist: https://www.youtube.com/playlist?list=PL7RjIaSLWh5kDiPVMUSyhvFaXL3NoXOe4
• Paul’s website: https://sabinanalytics.com/
• Paul on GitHub: https://github.com/sabinanalytics 
• Paul on Linkedin: https://www.linkedin.com/in/rpaulsabin/
• Paul on Twitter: https://twitter.com/SabinAnalytics
• Paul on Google Scholar: https://scholar.google.com/citations?user=wAezxZ4AAAAJ&hl=en
• Soccer Power Ratings & Projections: https://sabinanalytics.com/ratings/soccer/
• Estimating player value in American football using plus–minus models: https://www.degruyter.com/document/doi/10.1515/jqas-2020-0033/html
• World Football R Package: https://github.com/JaseZiv/worldfootballR

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

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

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

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

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

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

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

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary and Blake Walters.

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

Takeaways:

• Amortized Bayesian inference combines deep learning and statistics to make posterior inference fast and trustworthy.
• Bayesian neural networks can be used for full Bayesian inference on neural network weights.
• Amortized Bayesian inference decouples the training phase and the posterior inference phase, making posterior sampling much faster.
• BayesFlow is a Python library for amortized Bayesian workflows, providing a user-friendly interface and modular architecture.
• Self-consistency loss is a technique that combines simulation-based inference and likelihood-based Bayesian inference, with a focus on amortization
• The BayesFlow package aims to make amortized Bayesian inference more accessible and provides sensible default values for neural networks.
• Deep fusion techniques allow for the fusion of multiple sources of information in neural networks.
• Generative models that are expressive and have one-step inference are an emerging topic in deep learning and probabilistic machine learning.
• Foundation models, which have a large training set and can handle out-of-distribution cases, are another intriguing area of research.

Chapters:

00:00 Introduction to Amortized Bayesian Inference

07:39 Bayesian Neural Networks

11:47 Amortized Bayesian Inference and Posterior Inference

23:20 BayesFlow: A Python Library for Amortized Bayesian Workflows

38:15 Self-consistency loss: Bridging Simulation-Based Inference and Likelihood-Based Bayesian Inference

41:35 Amortized Bayesian Inference

43:53 Fusing Multiple Sources of Information

45:19 Compensating for Missing Data

56:17 Emerging Topics: Expressive Generative Models and Foundation Models

01:06:18 The Future of Deep Learning and Probabilistic Machine Learning

Links from the show:

• Marvin’s website: https://www.marvinschmitt.com/
• Marvin on GitHub: https://github.com/marvinschmitt
• Marvin on Linkedin: https://www.linkedin.com/in/marvin-schmitt/
• Marvin on Twitter: https://twitter.com/MarvinSchmittML
• The BayesFlow package for amortized Bayesian workflows: https://bayesflow.org/ 
• BayesFlow Forums for users: https://discuss.bayesflow.org
• BayesFlow software paper (JOSS): https://joss.theoj.org/papers/10.21105/joss.05702
• Tutorial on amortized Bayesian inference with BayesFlow (Python): https://colab.research.google.com/drive/1ub9SivzBI5fMbSTwVM1pABsMlRupgqRb?usp=sharing 
• Towards Reliable Amortized Bayesian Inference: https://www.marvinschmitt.com/speaking/pdf/slides_reliable_abi_botb.pdf
• Expand the model space that we amortize over (multiverse analyses, power scaling, …): “Sensitivity-Aware Amortized Bayesian Inference” https://arxiv.org/abs/2310.11122
• Use heterogeneous data sources in amortized inference: “Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference” https://arxiv.org/abs/2311.10671
• Use likelihood density information (explicit or even learned on the fly): “Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference”  https://arxiv.org/abs/2310.04395
• LBS #98 Fusing Statistical Physics, Machine Learning & Adaptive MCMC, with Marylou Gabrié: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/
• LBS #101 Black Holes Collisions & Gravitational Waves, with LIGO Experts Christopher Berry & John Veitch: https://learnbayesstats.com/episode/101-black-holes-collisions-gravitational-waves-ligo-experts-christopher-berry-john-veitch/
• Deep Learning book: https://www.deeplearningbook.org/
• Statistical Rethinking: https://xcelab.net/rm/

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

If there is one guest I don’t need to introduce, it’s mister Andrew Gelman. So… I won’t! I will refer you back to his two previous appearances on the show though, because learning from Andrew is always a pleasure. So go ahead and listen to episodes 20 and 27.

In this episode, Andrew and I discuss his new book, Active Statistics, which focuses on teaching and learning statistics through active student participation. Like this episode, the book is divided into three parts: 1) The ideas of statistics, regression, and causal inference; 2) The value of storytelling to make statistical concepts more relatable and interesting; 3) The importance of teaching statistics in an active learning environment, where students are engaged in problem-solving and discussion.

And Andrew is so active and knowledgeable that we of course touched on a variety of other topics — but for that, you’ll have to listen ;)

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

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary and Blake Walters.

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

Takeaways:

- Active learning is essential for teaching and learning statistics.

- Storytelling can make statistical concepts more relatable and interesting.

- Teaching statistics in an active learning environment engages students in problem-solving and discussion.

- The book Active Statistics includes 52 stories, class participation activities, computer demonstrations, and homework assignments to facilitate active learning.

- Active learning, where students actively engage with the material through activities and discussions, is an effective approach to teaching statistics.

- The flipped classroom model, where students read and prepare before class and engage in problem-solving activities during class, can enhance learning and understanding.

- Clear organization and fluency in teaching statistics are important for student comprehension and engagement.

- Visualization plays a crucial role in understanding statistical concepts and aids in comprehension.

- The future of statistical education may involve new approaches and technologies, but the challenge lies in finding effective ways to teach basic concepts and make them relevant to real-world problems.

Chapters:

00:00 Introduction and Background

08:09 The Importance of Stories in Statistics Education

30:28 Using 'Two Truths and a Lie' to Teach Logistic Regression

38:08 The Power of Storytelling in Teaching Statistics

57:26 The Importance of Visualization in Understanding Statistics

01:07:03 The Future of Statistical Education

Links from the show:

• Andrew’s website: http://www.stat.columbia.edu/~gelman/
• Andrew’s blog: https://statmodeling.stat.columbia.edu/ 
• Twitter links to blog posts: https://twitter.com/statmodeling
• Active Statistics page: http://www.stat.columbia.edu/~gelman/active-statistics/
• “Two truths and a lie” as a class-participation activity: http://www.stat.columbia.edu/~gelman/research/published/truths_paper.pdf
• Rohan Alexander’s book, Telling Stories with Data:  https://tellingstorieswithdata.com/
• Use code ACTSTAT24 to buy Active Statistics with 20% off through July 15, 2024: www.cambridge.org/9781009436212
• LBS #27, Modeling the US Presidential Elections, with Andrew Gelman & Merlin Heidemanns: https://learnbayesstats.com/episode/27-modeling-the-us-presidential-elections-with-andrew-gelman-merlin-heidemanns/
• LBS #20 Regression and Other Stories, with Andrew Gelman, Jennifer Hill & Aki Vehtari: https://learnbayesstats.com/episode/20-regression-and-other-stories-with-andrew-gelman-jennifer-hill-aki-vehtari/
• Slamming the sham – A Bayesian model for adaptive adjustment with noisy control data: http://www.stat.columbia.edu/~gelman/research/unpublished/chickens.pdf

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.