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:

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

Chapters:

00:00 Introduction to Bayesian Statistics and Bob Kubinec

06:01 Bob's Academic Journey and Research Focus

12:40 Measuring Corruption: Challenges and Methods

18:54 Transition from Government to Academia

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

34:51 Bayesian Methods in Political Science Research

42:08 Bayesian Methods in COVID Measurement

51:12 The Journey of Writing a Novel

01:00:24 The Intersection of Fiction and Academia

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, 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 and Stefan.

Links from the show:

• Robert’s website (includes blog posts): https://www.robertkubinec.com/
• Robert on GitHub: https://github.com/saudiwin
• Robert on Linkedin: https://www.linkedin.com/in/robert-kubinec-9191a9a/
• Robert on Google Scholar: https://scholar.google.com/citations?user=bhOaXR4AAAAJ&hl=en
• Robert on Twitter: https://x.com/rmkubinec
• Robert on Bluesky: https://bsky.app/profile/rmkubinec.bsky.social
• The Bayesian Hitman: https://www.amazon.com/Bayesian-Hitman-Robert-M-Kubinec/dp/B0D6M4WNRZ/
• Ordbetareg overview: https://www.robertkubinec.com/ordbetareg
• Idealstan – this isn’t out yet, but you can access an older working paper here: https://osf.io/preprints/osf/8j2bt
• Ordinal Regression tutorial, Michael Betancourt: https://betanalpha.github.io/assets/case_studies/ordinal_regression.html
• Andrew Heiss blog: https://www.andrewheiss.com/blog/

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:

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

Chapters:

00:00 Introduction to the Live Episode

02:55 Meet the Stan Core Developers

05:47 Brian Ward's Journey into Bayesian Statistics

09:10 Charles Margossian's Contributions to Stan

11:49 Recent Projects and Innovations in Stan

15:07 User-Friendly Features and Enhancements

18:11 Understanding Tuples and Their Importance

21:06 Challenges for Beginners in Stan

24:08 Pedagogical Approaches to Bayesian Statistics

30:54 Optimizing Monte Carlo Estimators

32:24 Reimagining Stan's Structure

34:21 The Promise of Automatic Reparameterization

35:49 Exploring BridgeStan

40:29 The Future of Samplers in Stan

43:45 Evaluating New Algorithms

47:01 Specific Algorithms for Unique Problems

50:00 Understanding Model Performance

54:21 The Impact of Stan on Bayesian Research

Thank you to my Patrons for making this episode possible!

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

Links from the show:

• Come see the show live at PyData NYC: https://pydata.org/nyc2024/
• LBS #90, Demystifying MCMC & Variational Inference, with Charles Margossian: https://learnbayesstats.com/episode/90-demystifying-mcmc-variational-inference-charles-margossian/
• Charles' website: https://charlesm93.github.io/
• Charles on GitHub: https://github.com/charlesm93
• Charles on LinkedIn: https://www.linkedin.com/in/charles-margossian-3428935b/
• Charles on Google Scholar: https://scholar.google.com/citations?user=nPtLsvIAAAAJ&hl=en
• Charles on Twitter: https://x.com/charlesm993
• Brian's website: https://brianward.dev/
• Brian on GitHub: https://github.com/WardBrian
• Brian on LinkedIn: https://www.linkedin.com/in/ward-brianm/
• Brian on Google Scholar: https://scholar.google.com/citations?user=bzosqW0AAAAJ&hl=en
• Brian on Twitter: https://x.com/ward_brianm
• Bob Carpenter's reflections on StanCon: https://statmodeling.stat.columbia.edu/category/bayesian-statistics/

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:

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

Chapters:

00:00 Introduction to Bayesian Experimental Design

07:51 Understanding Bayesian Experimental Design

19:58 Computational Challenges in Bayesian Experimental Design

28:47 Innovations in Bayesian Experimental Design

40:43 Practical Applications of Bayesian Experimental Design

52:12 Future of Bayesian Experimental Design

01:01:17 Real-World Applications and Impact

Thank you to my Patrons for making this episode possible!

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

Links from the show:

• Come see the show live at PyData NYC: https://pydata.org/nyc2024/
• Desi’s website: https://desirivanova.com/
• Desi on GitHub: https://github.com/desi-ivanova
• Desi on Google Scholar: https://scholar.google.com/citations?user=AmX6sMIAAAAJ&hl=en
• Desi on Linkedin: https://www.linkedin.com/in/dr-ivanova/
• Desi on Twitter: https://x.com/desirivanova
• LBS #34, Multilevel Regression, Post-stratification & Missing Data, with Lauren Kennedy: https://learnbayesstats.com/episode/34-multilevel-regression-post-stratification-missing-data-lauren-kennedy/
• LBS #35, The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner/
• LBS #45, Biostats & Clinical Trial Design, with Frank Harrell:https://learnbayesstats.com/episode/45-biostats-clinical-trial-design-frank-harrell/
• 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/
• Bayesian Experimental Design (BED) with BayesFlow and PyTorch: https://github.com/stefanradev93/BayesFlow/blob/dev/examples/michaelis_menten_BED_tutorial.ipynb
• Paper – Modern Bayesian Experimental Design: https://arxiv.org/abs/2302.14545
• Paper – Optimal experimental design; Formulations and computations: https://arxiv.org/pdf/2407.16212
• Information theory, inference and learning algorithms, by the great late Sir David MacKay:  https://www.inference.org.uk/itprnn/book.pdf
• Patterns, Predictions and Actions, Moritz Hard and Ben Recht  https://mlstory.org/index.html

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:

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

Chapters:

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

06:30 Building an Analytics Department

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

28:00 Challenges and Innovations in Soccer Analytics 

42:00 Player Health, Injury Prevention, and Training 

55:00 The Evolution of Data-Driven Strategies

01:10:00 Future of Analytics in Sports

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, 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 and Gary Clarke.

Links from the show:

• LBS Sports Analytics playlist: https://www.youtube.com/playlist?list=PL7RjIaSLWh5kDiPVMUSyhvFaXL3NoXOe4
• Ravi on Linkedin: https://www.linkedin.com/in/ravi-ramineni-3798374/
• Ravi on Twitter: https://x.com/analyseFooty
• Decisions in Football - The Power of Compounding | StatsBomb Conference 2023: https://www.youtube.com/watch?v=D7CXtwDg9lM
• The Signal and the Noise: https://www.amazon.com/Signal-Noise-Many-Predictions-Fail-but/dp/0143125087
• PreliZ – A tool-box for prior elicitation: https://preliz.readthedocs.io/en/latest/
• Ravi talking on Ted Knutson's podcast: https://open.spotify.com/episode/1exLBfyFf0d1dm2IaXkd2v
• More about Ravi's work at the Seattle Sounders: https://www.trumedianetworks.com/expected-value-podcast/ravi-ramineni

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:

• 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.