Learning Bayesian Statistics Learn Bayes Stats

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My Intuitive Bayes Online Courses1:1 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...

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
My Intuitive Bayes Online Courses1:1 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

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
My Intuitive Bayes Online Courses1:1 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...

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
My Intuitive Bayes Online Courses1:1 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...

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
My Intuitive Bayes Online Courses1:1 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...

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
My Intuitive Bayes Online Courses1:1 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...