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

• Intro to Bayes Course (first 2 lessons free)
• Advanced Regression Course (first 2 lessons free)

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

• The hype around AI in science often fails to deliver practical results.
• Bayesian deep learning combines the strengths of deep learning and Bayesian statistics.
• Fine-tuning LLMs with Bayesian methods improves prediction calibration.
• There is no single dominant library for Bayesian deep learning yet.
• Real-world applications of Bayesian deep learning exist in various fields.
• Prior knowledge is crucial for the effectiveness of Bayesian deep learning.
• Data efficiency in AI can be enhanced by incorporating prior knowledge.
• Generative AI and Bayesian deep learning can inform each other.
• The complexity of a problem influences the choice between Bayesian and traditional deep learning.
• Meta-learning enhances the efficiency of Bayesian models.
• PAC-Bayesian theory merges Bayesian and frequentist ideas.
• Laplace inference offers a cost-effective approximation.
• Subspace inference can optimize parameter efficiency.
• Bayesian deep learning is crucial for reliable predictions.
• Effective communication of uncertainty is essential.
• Realistic benchmarks are needed for Bayesian methods
• Collaboration and communication in the AI community are vital.

Chapters:

00:00 Introduction to Bayesian Deep Learning

04:24 Vincent Fortuin’s Journey to Bayesian Deep Learning

11:52 Understanding Bayesian Deep Learning

16:29 Current Landscape of Bayesian Libraries

21:11 Real-World Applications of Bayesian Deep Learning

23:33 When to Use Bayesian Deep Learning

28:22 Data Efficiency in AI and Generative Modeling

30:18 Integrating Bayesian Knowledge into Generative Models

31:44 The Role of Meta-Learning in Bayesian Deep Learning

34:06 Understanding Pack Bayesian Theory

37:55 Algorithms for Bayesian Deep Learning Models

42:10 Advancements in Efficient Inference Techniques

48:21 The Importance of Uncertainty in AI Models

51:41 Advice for Aspiring Researchers in AI

54:08 Future Directions in AI for Science

Thank you to my Patrons for making this episode possible!

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

Links from the show:

• Vincent’s website: https://fortuin.github.io/
• Vincent on Linkedin: https://www.linkedin.com/in/vincent-fortuin-42426b134/
• Vincent on GitHub: https://github.com/fortuin
• Vincent on Medium: https://medium.com/@vincefort
• Vincent on BlueSky: https://bsky.app/profile/vincefort.bsky.social 
• 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/
• Position paper on Bayesian deep learning: https://proceedings.mlr.press/v235/papamarkou24b.html
• Position paper on generative AI: https://arxiv.org/abs/2403.00025
• BNN review paper: https://arxiv.org/abs/2309.16314
• Priors in BDL review paper: https://onlinelibrary.wiley.com/doi/10.1111/insr.12502
• BayesFlow: https://bayesflow.org/
• BayesianTorch: https://github.com/IntelLabs/bayesian-torch
• Laplace Torch: https://aleximmer.github.io/Laplace/
• TyXe: https://github.com/TyXe-BDL/TyXe 
• Introduction to PAC-Bayes: https://arxiv.org/abs/2110.11216
• Training GPT2 with Bayesian methods: https://proceedings.mlr.press/v235/shen24b.html
• Bayesian fine-tuning for LLMs: https://arxiv.org/abs/2405.03425 
• Try out NormalizingFlow initialization with Nutpie: https://discourse.pymc.io/t/new-experimental-sampling-algorithm-fisher-hmc-in-nutpie-for-pymc-and-stan-models/16114/5

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!

• Intro to Bayes Course (first 2 lessons free)
• Advanced Regression Course (first 2 lessons free)

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

• Matt emphasizes the importance of Bayesian statistics in scenarios with limited data.
• Communicating insights to coaches is a crucial skill for data analysts.
• Building a data team requires understanding the needs of the coaching staff.
• Player recruitment is a significant focus in football analytics.
• The integration of data science in sports is still evolving.
• Effective data modeling must consider the practical application in games.
• Collaboration between data analysts and coaches enhances decision-making.
• Having a robust data infrastructure is essential for efficient analysis.
• The landscape of sports analytics is becoming increasingly competitive. 
• Player recruitment involves analyzing various data models.
• Biases in traditional football statistics can skew player evaluations.
• Statistical techniques should leverage the structure of football data.
• Tracking data opens new avenues for understanding player movements.
• The role of data analysis in football will continue to grow.
• Aspiring analysts should focus on curiosity and practical experience.

Chapters:

00:00 Introduction to Football Analytics and Matt's Journey

04:54 The Role of Bayesian Methods in Football

10:20 Challenges in Communicating Data Insights

17:03 Building Relationships with Coaches

22:09 The Structure of the Data Team at Como

26:18 Focus on Player Recruitment and Transfer Strategies

28:48 January Transfer Window Insights

30:54 Biases in Football Data Analysis

34:11 Comparative Analysis of Men's and Women's Football

36:55 Statistical Techniques in Football Analysis

42:48 The Impact of Tracking Data on Football Analysis

45:49 The Future of Data-Driven Football Strategies

47:27 Advice for Aspiring Football Analysts

51:29 Future Projects in Football 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, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık and Suyog Chandramouli.

Links from the show:

• Matt on Linkedin: https://www.linkedin.com/in/matthew-penn-732551232/
• Matt on Google Scholar: https://scholar.google.com/citations?user=oY7jC7UAAAAJ&hl=en
• LBS #117, Unveiling the Power of Bayesian Experimental Design, with Desi Ivanova: https://learnbayesstats.com/episode/117-unveiling-power-bayesian-experimental-design-desi-ivanova/

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!

• Intro to Bayes Course (first 2 lessons free)
• Advanced Regression Course (first 2 lessons free)

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Thank you to my Patrons for making this episode possible!

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

Takeaways:

• Sharks play a crucial role in maintaining healthy ocean ecosystems.
• Bayesian statistics are particularly useful in data-poor environments like ecology.
• Teaching Bayesian statistics requires a shift in mindset from traditional statistical methods.
• The shark meat trade is significant and often overlooked.
• Ray meat trade is as large as shark meat trade, with specific markets dominating.
• Understanding the ecological roles of species is essential for effective conservation.
• Causal language is important in ecological research and should be encouraged.
• Evidence-driven decision-making is crucial in balancing human and ecological needs.
• Expert opinions are crucial for understanding species composition in landings.
• Trade dynamics are influenced by import preferences and species availability.
• Bayesian modeling allows for the incorporation of various data sources and expert knowledge.
• Field data collection is essential for validating model assumptions.
• The complexity of trade relationships necessitates a nuanced approach to modeling.
• Understanding the impact of management interventions on landings is critical.
• The role of scientists in informing policy is vital for effective conservation efforts.

Chapters:

00:00 Introduction to Marine Biology and Statistics

04:33 The Role of Bayesian Statistics in Marine Research

10:09 Challenges in Teaching Bayesian Statistics

21:58 The Importance of Sharks in Ecosystems

26:35 Understanding Shark Meat Trade and Conservation

32:09 The Trade in Ray and Shark Meat

36:18 Modeling Landings and Trade

42:56 Challenges in Data Integration

44:50 Running Complex Models

51:57 Expert Elicitation and Prior Construction

55:52 Future Directions and Research

56:46 Reflections on Science and Policy

Links from the show:

• Fisheries Lab: https://ifisheries.org/?page_id=83
• 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

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!

• Intro to Bayes Course (first 2 lessons free)
• Advanced Regression Course (first 2 lessons free)

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

• Marketing analytics is crucial for understanding customer behavior.
• PyMC Marketing offers tools for customer lifetime value analysis.
• Media mix modeling helps allocate marketing spend effectively.
• Customer Lifetime Value (CLV) models are essential for understanding long-term customer behavior.
• Productionizing models is essential for real-world applications.
• Productionizing models involves challenges like model artifact storage and version control.
• MLflow integration enhances model tracking and management.
• The open-source community fosters collaboration and innovation.
• Understanding time series is vital in marketing analytics.
• Continuous learning is key in the evolving field of data science.

Chapters:

00:00 Introduction to Will Dean and His Work

10:48 Diving into PyMC Marketing

17:10 Understanding Media Mix Modeling

25:54 Challenges in Productionizing Models

35:27 Exploring Customer Lifetime Value Models

44:10 Learning and Development in Data Science

Thank you to my Patrons for making this episode possible!

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

Links from the show:

• Get your ticket for Field of Play: https://www.fieldofplay.co.uk/tickets
• Will's website: https://wd60622.github.io/blog/
• Will on GitHub: https://github.com/wd60622/
• Will on Linkedin: https://www.linkedin.com/in/williambdean/
• PyMC-Marketing: https://www.pymc-marketing.io/en/stable/

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!

• Intro to Bayes Course (first 2 lessons free)
• Advanced Regression Course (first 2 lessons free)

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Thank you to my Patrons for making this episode possible!

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

Takeaways:

• The evolution of sports modeling is tied to the availability of high-frequency data.
• Bayesian methods are valuable in handling messy, hierarchical data.
• Communication between data scientists and decision-makers is crucial for effective model use.
• Models are often wrong, and learning from mistakes is part of the process.
• Simplicity in models can sometimes yield better results than complexity.
• The integration of analytics in sports is still developing, with opportunities in various sports.
• Transparency in research and development teams enhances decision-making.
• Understanding uncertainty in models is essential for informed decisions.
• The balance between point estimates and full distributions is a challenge.
• Iterative model development is key to improving analytics in sports.
• It's important to avoid falling in love with a single model.
• Data simulation can validate model structures before real data is used.
• Gaussian processes offer flexibility in modeling without strict functional forms.
• Structural time series help separate projection from observation noise.
• Transitioning from sports analytics to consulting opens new opportunities.
• Continuous learning is essential in the field of statistics.
• The demand for Bayesian methods is growing across various industries.
• Community-driven projects can lead to innovative solutions.

Chapters:

03:07 The Evolution of Modeling in Sports Analytics

06:03 Transitioning from Academia to Sports Modeling

08:56 The Role of Bayesian Methods in Sports Analytics

11:49 Communicating Models and Insights to Decision Makers

15:12 Learning from Mistakes in Model Development

18:06 The Importance of Model Flexibility and Iteration

21:02 Utilizing Simulation for Model Validation

23:50 Choosing the Right Model Structure for Data

27:04 Starting with Simple Models and Building Complexity

29:29 Advancements in Gaussian Processes and PyMC

31:54 Exploring Structural Time Series and GPs

37:34 Transitioning to PyMC Labs and New Opportunities

42:40 Innovations in Variational Inference Methods

48:50 Future Vision for PyMC and Community Engagement

50:43 Surprises in Bayesian Methods Adoption

54:08 Reflections on Problem Solving and Influential Figures

Links from the show:

• Alex's and Chris’ GP tutorial at PyData NYC: https://youtu.be/u6I5pN_Q6r4?si=5IzrQB_0k30Rmzhu
• Chris on GitHub: https://github.com/fonnesbeck
• Chris on Linkedin: https://www.linkedin.com/in/christopher-fonnesbeck-374a492a/
• Chris on Blue Sky: https://bsky.app/profile/fonnesbeck.bsky.social
• Developing Hierarchical Models for Sports Analytics: https://www.pymc-labs.com/blog-posts/2023-09-15-Hierarchical-models-Chris-Fonnesbeck/
• Beyond Moneyball: Phillies Data Scientist Give Students a Real-World Look at How Today’s MLB Teams Use Data: https://datascience.virginia.edu/news/beyond-moneyball-phillies-data-scientist-give-students-real-world-look-how-todays-mlb-teams
• HSGP Reference & First Steps: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Basic.html
• HSGP Advanced Usage: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Advanced.html
• Data simulation with PyMC: https://tomicapretto.com/posts/2024-11-01_pymc-data-simulation/
• LBS #124 State Space Models & Structural Time Series, with Jesse Grabowski: https://learnbayesstats.com/episode/124-state-space-models-structural-time-series-jesse-grabowski

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 offers a robust framework for econometric modeling.
• State space models provide a comprehensive way to understand time series data.
• Gaussian random walks serve as a foundational model in time series analysis.
• Innovations represent external shocks that can significantly impact forecasts.
• Understanding the assumptions behind models is key to effective forecasting.
• Complex models are not always better; simplicity can be powerful.
• Forecasting requires careful consideration of potential disruptions. Understanding observed and hidden states is crucial in modeling.
• Latent abilities can be modeled as Gaussian random walks.
• State space models can be highly flexible and diverse.
• Composability allows for the integration of different model components.
• Trends in time series should reflect real-world dynamics.
• Seasonality can be captured through Fourier bases.
• AR components help model residuals in time series data.
• Exogenous regression components can enhance state space models.
• Causal analysis in time series often involves interventions and counterfactuals.
• Time-varying regression allows for dynamic relationships between variables.
• Kalman filters were originally developed for tracking rockets in space.
• The Kalman filter iteratively updates beliefs based on new data.
• Missing data can be treated as hidden states in the Kalman filter framework.
• The Kalman filter is a practical application of Bayes' theorem in a sequential context.
• Understanding the dynamics of systems is crucial for effective modeling.
• The state space module in PyMC simplifies complex time series modeling tasks.

Chapters:

00:00 Introduction to Jesse Krabowski and Time Series Analysis

04:33 Jesse's Journey into Bayesian Statistics

10:51 Exploring State Space Models

18:28 Understanding State Space Models and Their Components

40:39 Composability of State Space Models

48:36 Understanding Trends and Derivatives

52:35 The Importance of Seasonality in Time Series

56:41 Components of Time Series Analysis

01:00:46 Exogenous Regression in State Space Models

01:06:41 Impulse Response Functions and Causality

01:11:30 Why Kalman Filter Is So Powerful

01:24:28 Future Directions and Applications

Thank you to my Patrons for making this episode possible!

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

Links from the show:

• Jesse on GitHub: https://github.com/jessegrabowski
• Jesse on LinkedIn: www.linkedin.com/in/jessegrabowski
• Jesse on Google Scholar: https://scholar.google.com/citations?user=vOCjGPwAAAAJ&hl=en
• State space presentation repo: https://github.com/jessegrabowski/statespace-presentation/tree/main
• Try the statespace module on pymc-experimental: https://github.com/pymc-devs/pymc-experimental
• Durbin, James, and Siem Jan Koopman. Time series analysis by state space methods, Oxford, 2012: https://academic.oup.com/book/16563?login=false
• Hyndman, Rob and George Athanasopoulos, Forecasting: Principals and Practice, 3rd Edition. Otexts, 2018: https://otexts.com/fpp3/
• Roger Labbe, Kalman and Bayesian Filters in Python: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
• Quantecon.org: https://quantecon.org/
• Lecture on Kalman Filtering: https://python.quantecon.org/kalman.html
• Mamba – Linear-Time Sequence Modeling with State Spaces (state spaces in machine learning): https://arxiv.org/abs/2312.00752
• Paper explanation: https://www.youtube.com/watch?v=9dSkvxS2EB0
• Good lecture on the statistics of the Kalman filter: https://www.youtube.com/watch?v=8lPBkkbtNW8
• And on structural state space models: https://www.youtube.com/watch?v=2vf-d_fRCXs

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:

• BART models are non-parametric Bayesian models that approximate functions by summing trees.
• BART is recommended for quick modeling without extensive domain knowledge.
• PyMC-BART allows mixing BART models with various likelihoods and other models.
• Variable importance can be easily interpreted using BART models.
• PreliZ aims to provide better tools for prior elicitation in Bayesian statistics.
• The integration of BART with Bambi could enhance exploratory modeling.
• Teaching Bayesian statistics involves practical problem-solving approaches.
• Future developments in PyMC-BART include significant speed improvements.
• Prior predictive distributions can aid in understanding model behavior.
• Interactive learning tools can enhance understanding of statistical concepts.
• Integrating PreliZ with PyMC improves workflow transparency.
• Arviz 1.0 is being completely rewritten for better usability.
• Prior elicitation is crucial in Bayesian modeling.
• Point intervals and forest plots are effective for visualizing complex data.

Chapters:

00:00 Introduction to Osvaldo Martin and Bayesian Statistics

08:12 Exploring Bayesian Additive Regression Trees (BART)

18:45 Prior Elicitation and the PreliZ Package

29:56 Teaching Bayesian Statistics and Future Directions

45:59 Exploring Prior Predictive Distributions

52:08 Interactive Modeling with PreliZ

54:06 The Evolution of ArviZ

01:01:23 Advancements in ArviZ 1.0

01:06:20 Educational Initiatives in Bayesian Statistics

01:12:33 The Future of Bayesian Methods

Thank you to my Patrons for making this episode possible!

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

Links from the show:

• LBS #1 Bayes, open-source and bioinformatics, with Osvaldo Martin: https://learnbayesstats.com/episode/1-bayes-open-source-and-bioinformatics-with-osvaldo-martin/
• LBS #58 Bayesian Modeling and Computation, with Osvaldo Martin, Ravin Kumar and Junpeng Lao: https://learnbayesstats.com/episode/58-bayesian-modeling-computation-osvaldo-martin-ravin-kumar-junpeng-lao/
• LBS #112 Advanced Bayesian Regression, with Tomi Capretto: https://learnbayesstats.com/episode/112-advanced-bayesian-regression-tomi-capretto/
• Osvaldo's website: https://aloctavodia.github.io/
• Osvaldo on GitHub: https://github.com/aloctavodia
• Osvaldo on LinkedIn: https://www.linkedin.com/in/osvaldo-martin-447a662b1/
• Osvaldo on Google Scholar: https://scholar.google.com/citations?user=WUvDNnkAAAAJ
• Osvaldo on Mastodon: https://bayes.club/@aloctavodia
• Osvaldo on BlueSky: https://bsky.app/profile/aloctavodia.bsky.social
• PyMC-BART package: https://www.pymc.io/projects/bart/en/latest/index.html
• PyMC-BART paper: https://arxiv.org/abs/2206.03619
• PreliZ for prior elicitation: https://preliz.readthedocs.io/en/latest/
• Prior Knowledge Elicitation: The Past, Present, and Future: https://projecteuclid.org/journals/bayesian-analysis/advance-publication/Prior-Knowledge-Elicitation-The-Past-Present-and-Future/10.1214/23-BA1381.full
• ArviZ 1.0 repository: https://arviz-plots.readthedocs.io/en/latest/
• Practical MCMC course: https://www.intuitivebayes.com/practical-mcmc
• Cohort Retention Analysis with BART: https://juanitorduz.github.io/retention_bart/
• HSGP Reference & First Steps: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Basic.html
• HSGP Advanced Usage: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Advanced.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:

• Effective data science education requires feedback and rapid iteration.
• Building LLM applications presents unique challenges and opportunities.
• The software development lifecycle for AI differs from traditional methods.
• Collaboration between data scientists and software engineers is crucial.
• Hugo's new course focuses on practical applications of LLMs.
• Continuous learning is essential in the fast-evolving tech landscape.
• Engaging learners through practical exercises enhances education.
• POC purgatory refers to the challenges faced in deploying LLM-powered software.
• Focusing on first principles can help overcome integration issues in AI.
• Aspiring data scientists should prioritize problem-solving over specific tools.
• Engagement with different parts of an organization is crucial for data scientists.
• Quick paths to value generation can help gain buy-in for data projects.
• Multimodal models are an exciting trend in AI development.
• Probabilistic programming has potential for future growth in data science.
• Continuous learning and curiosity are vital in the evolving field of data science.

Chapters:

09:13 Hugo's Journey in Data Science and Education

14:57 The Appeal of Bayesian Statistics

19:36 Learning and Teaching in Data Science

24:53 Key Ingredients for Effective Data Science Education

28:44 Podcasting Journey and Insights

36:10 Building LLM Applications: Course Overview

42:08 Navigating the Software Development Lifecycle

48:06 Overcoming Proof of Concept Purgatory

55:35 Guidance for Aspiring Data Scientists

01:03:25 Exciting Trends in Data Science and AI

01:10:51 Balancing Multiple Roles in Data Science

01:15:23 Envisioning Accessible Data Science for All

Thank you to my Patrons for making this episode possible!

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

Links from the show:

• Alex's last paper, “Unveiling True Talent: The Soccer Factor Model for Skill Evaluation”: https://arxiv.org/abs/2412.05911
• Associated code and data: https://github.com/AlexAndorra/football-modeling/tree/main/40_submissions/MIT_Sloan_2025/01_Paper
• Hugo on Blue Sky: https://bsky.app/profile/hugobowne.bsky.social
• Hugo’s website: https://hugobowne.github.io/
• Hugo on Linkedin: https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/
• Hugo on GitHub: https://github.com/hugobowne
• Vanishing Gradients podcast: https://vanishinggradients.fireside.fm/hosts/hugobowne
• High Signal podcast: https://high-signal.delphina.ai/ 
• 25% discount on Hugo’s course on Building LLM Applications: https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=LEARNBAYES25
• Lightning lessons if people want to get a sense of Hugo's  teaching style and content:
• https://maven.com/p/38a781/building-with-gen-ai-from-first-principles?utm_medium=ll_share_link&utm_source=instructor
• https://maven.com/p/a6f9bf/mastering-llm-application-testing?utm_medium=ll_share_link&utm_source=instructor

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:

• CFA is commonly used in psychometrics to validate theoretical constructs.
• Theoretical structure is crucial in confirmatory factor analysis.
• Bayesian approaches offer flexibility in modeling complex relationships.
• Model validation involves both global and local fit measures.
• Sensitivity analysis is vital in Bayesian modeling to avoid skewed results.
• Complex models should be justified by their ability to answer specific questions.
• The choice of model complexity should balance fit and theoretical relevance. Fitting models to real data builds confidence in their validity.
• Divergences in model fitting indicate potential issues with model specification.
• Factor analysis can help clarify causal relationships between variables.
• Survey data is a valuable resource for understanding complex phenomena.
• Philosophical training enhances logical reasoning in data science.
• Causal inference is increasingly recognized in industry applications.
• Effective communication is essential for data scientists.
• Understanding confounding is crucial for accurate modeling.

Chapters:

10:11 Understanding Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA)

20:11 Application of SEM and CFA in HR Analytics

30:10 Challenges and Advantages of Bayesian Approaches in SEM and CFA

33:58 Evaluating Bayesian Models

39:50 Challenges in Model Building

44:15 Causal Relationships in SEM and CFA

49:01 Practical Applications of SEM and CFA

51:47 Influence of Philosophy on Data Science

54:51 Designing Models with Confounding in Mind

57:39 Future Trends in Causal Inference

01:00:03 Advice for Aspiring Data Scientists

01:02:48 Future Research Directions

Thank you to my Patrons for making this episode possible!

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

Links from the show:

• Modeling Webinar – Bayesian Causal Inference & Propensity Scores: https://www.youtube.com/watch?v=y9BeOr0AETw&list=PL7RjIaSLWh5lDvhGf6qs_im0fRzOeFN5_&index=9
• LBS #102, Bayesian Structural Equation Modeling & Causal Inference in Psychometrics, with Ed Merkle: https://learnbayesstats.com/episode/102-bayesian-structural-equation-modeling-causal-inference-psychometrics-ed-merkle/ 
• Nate’s website: https://nathanielf.github.io/
• Nate on GitHub: https://github.com/NathanielF
• Nate on Linkedin: https://www.linkedin.com/in/nathaniel-forde-2477a265/
• Nate on Twitter: https://x.com/forde_nathaniel
• Confirmatory Factor Analysis and Structural Equation Models in Psychometrics: https://www.pymc.io/projects/examples/en/latest/case_studies/CFA_SEM.html
• Measurement, Latent Factors and the Garden of Forking Paths: https://nathanielf.github.io/posts/post-with-code/CFA_AND_SEM/CFA_AND_SEM.html
• Bayesian Non-parametric Causal Inference: https://www.pymc.io/projects/examples/en/latest/causal_inference/bayesian_nonparametric_causal.html
• Simpson’s paradox: https://www.pymc.io/projects/examples/en/latest/causal_inference/GLM-simpsons-paradox.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 ;)

-------------------------

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-------------------------

Takeaways:

• Epidemiology focuses on health at various scales, while biology often looks at micro-level details.
• Bayesian statistics helps connect models to data and quantify uncertainty.
• Recent advancements in data collection have improved the quality of epidemiological research.
• Collaboration between domain experts and statisticians is essential for effective research.
• The COVID-19 pandemic has led to increased data availability and international cooperation.
• Modeling infectious diseases requires understanding complex dynamics and statistical methods.
• Challenges in coding and communication between disciplines can hinder progress.
• Innovations in machine learning and neural networks are shaping the future of epidemiology.
• The importance of understanding the context and limitations of data in research. 

Chapters:

00:00 Introduction to Bayesian Statistics and Epidemiology

03:35 Guest Backgrounds and Their Journey

10:04 Understanding Computational Biology vs. Epidemiology

16:11 The Role of Bayesian Statistics in Epidemiology

21:40 Recent Projects and Applications in Epidemiology

31:30 Sampling Challenges in Health Surveys

34:22 Model Development and Computational Challenges

36:43 Navigating Different Jargons in Survey Design

39:35 Post-COVID Trends in Epidemiology

42:49 Funding and Data Availability in Epidemiology

45:05 Collaboration Across Disciplines

48:21 Using Neural Networks in Bayesian Modeling

51:42 Model Diagnostics in Epidemiology

55:38 Parameter Estimation in Compartmental 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, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström and Stefan.

Links from the show:

• LBS #21, Gaussian Processes, Bayesian Neural Nets & SIR Models, with Elizaveta Semenova: https://learnbayesstats.com/episode/21-gaussian-processes-bayesian-neural-nets-sir-models-with-elizaveta-semenova/
• Liza’s website: https://www.elizaveta-semenova.com/
• Liza on GitHub: https://github.com/elizavetasemenova
• Liza on LinkedIn: https://www.linkedin.com/in/elizaveta-semenova/
• Liza on Google Scholar: https://scholar.google.com/citations?user=jqGIgFEAAAAJ&hl=en
• Chris' page: https://www.bdi.ox.ac.uk/Team/c-wymant
• Chris on GitHub: https://github.com/chrishiv
• Chris on LinkedIn: https://www.linkedin.com/in/chris-wymant-65661274/
• Chris on Blue Sky: https://bsky.app/profile/chriswymant.bsky.social
• Chris on Google Scholar: https://scholar.google.com/citations?user=OJ6t2UwAAAAJ&hl=en
• PriorVAE Paper: Explains how to build an emulator for a GP using a deep generative model (Variational Autoencoder, or VAE) and apply it within MCMC. Link to the paper
• PriorCVAE Paper: Builds on PriorVAE by encoding model parameters along with emulating stochastic process realisations. Includes examples for GPs, ODEs, and double-well models. Link to the paper
• StanCon 2024 Tutorial: A tutorial covering the basics of sequential decision-making, with a demo of Bayesian Optimization using Stan. Link to the tutorial
• Numpyro Course: Materials from a course Liza taught -- great for learning Numpyro. Link to the course
• aggVAE Paper: An application of PriorVAE to the problem of changing boundaries. Link to the paper

Transcript

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