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

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

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

• Setting appropriate priors is crucial to avoid overfitting in models.
• R-squared can be used effectively in Bayesian frameworks for model evaluation.
• Dynamic regression can incorporate time-varying coefficients to capture changing relationships.
• Predictively consistent priors enhance model interpretability and performance.
• Identifiability is a challenge in time series models.
• State space models provide structure compared to Gaussian processes.
• Priors influence the model's ability to explain variance.
• Starting with simple models can reveal interesting dynamics.
• Understanding the relationship between states and variance is key.
• State-space models allow for dynamic analysis of time series data.
• AI can enhance the process of prior elicitation in statistical models.

Chapters:

10:09 Understanding State Space Models

14:53 Predictively Consistent Priors

20:02 Dynamic Regression and AR Models

25:08 Inflation Forecasting

50:49 Understanding Time Series Data and Economic Analysis

57:04 Exploring Dynamic Regression Models

01:05:52 The Role of Priors

01:15:36 Future Trends in Probabilistic Programming

01:20:05 Innovations in Bayesian Model Selection

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki 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:

• David's website: https://davkoh.github.io/
• David on LinkedIn: https://www.linkedin.com/in/david-kohns-03984013b/
• David on GitHub: https://github.com/davkoh
• David on Google Scholar: https://scholar.google.com/citations?user=9gKE8e4AAAAJ&hl=en
• Dynamic Regression Case Study: https://davkoh.github.io/case-studies/01_dyn_reg/dyn_reg_casestudy5.html
• ARR2 Paper: https://projecteuclid.org/journals/bayesian-analysis/advance-publication/The-ARR2-Prior--Flexible-Predictive-Prior-Definition-for-Bayesian/10.1214/25-BA1512.full
• ARR2 Paper GitHub repository: https://github.com/n-kall/arr2/tree/main
• ARR2 StanCon talk: https://www.youtube.com/watch?v=8XBe2jrOKvw&list=PLCrWEzJgSUqzNzh6mjWsWUu-lSK59VXP6&index=29
• ARR2 Prior in PyMC: https://www.austinrochford.com/posts/r2-priors-pymc.html
• LBS #124 State Space Models & Structural Time Series, with Jesse Grabowski: https://learnbayesstats.com/episode/124-state-space-models-structural-time-series-jesse-grabowski
• 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 #74 Optimizing NUTS and Developing the ZeroSumNormal Distribution, with Adrian Seyboldt: https://learnbayesstats.com/episode/74-optimizing-nuts-developing-zerosumnormal-distribution-adrian-seyboldt
• Nutpie’s Normalizing Flows adaptation: https://pymc-devs.github.io/nutpie/nf-adapt.html

Transcript

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

Today’s clip is from episode 133 of the podcast, with Sean Pinkney & Adrian Seyboldt.

The conversation delves into the concept of Zero-Sum Normal and its application in statistical modeling, particularly in hierarchical models.

Alex, Sean and Adrian discuss the implications of using zero-sum constraints, the challenges of incorporating new data points, and the importance of distinguishing between sample and population effects.

They also explore practical solutions for making predictions based on population parameters and the potential for developing tools to facilitate these processes.

Get the full discussion here.

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

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

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

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

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

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

• Zero Sum constraints allow for better sampling and estimation in hierarchical models.
• Understanding the difference between population and sample means is crucial.
• A library for zero-sum normal effects would be beneficial.
• Practical solutions can yield decent predictions even with limitations.
• Cholesky parameterization can be adapted for positive correlation matrices.
• Understanding the geometry of sampling spaces is crucial.
• The relationship between eigenvalues and sampling is complex.
• Collaboration and sharing knowledge enhance research outcomes.
• Innovative approaches can simplify complex statistical problems.

Chapters:

03:35 Sean Pinkney's Journey to Bayesian Modeling

11:21 The Zero-Sum Normal Project Explained

18:52 Technical Insights on Zero-Sum Constraints

32:04 Handling New Elements in Bayesian Models

36:19 Understanding Population Parameters and Predictions

49:11 Exploring Flexible Cholesky Parameterization

01:07:23 Closing Thoughts and Future Directions

Thank you to my Patrons for making this episode possible!

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

• Sean's website: https://spinkney.github.io/
• Sean on LinkedIn: https://www.linkedin.com/in/sean-pinkney123/
• Sean on GitHub: https://github.com/spinkney
• Sean on BlueSky: https://bsky.app/profile/spinkney.bsky.social
• Sean on Mastodon: https://fosstodon.org/@spinkney
• Sean's talk at StanCon 2024: https://youtu.be/eE8Vqxs8OfQ?si=09-vNvCxpbz8enUj
• Flexible Cholesky Parameterization of Correlation Matrices: https://arxiv.org/abs/2405.07286
• Quantile Regressions in Stan: https://spinkney.github.io/posts/post-2-quantile-reg-series/post-2-quantile-reg-part-I/quantile-reg.html
• LBS #74 Optimizing NUTS and Developing the ZeroSumNormal Distribution, with Adrian Seyboldt: https://learnbayesstats.com/episode/74-optimizing-nuts-developing-zerosumnormal-distribution-adrian-seyboldt

Transcript

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

Today’s clip is from episode 132 of the podcast, with Tom Griffiths.

Tom and Alex Andorra discuss the fundamental differences between human intelligence and artificial intelligence, emphasizing the constraints that shape human cognition, such as limited data, computational resources, and communication bandwidth.

They explore how AI systems currently learn and the potential for aligning AI with human cognitive processes.

The discussion also delves into the implications of AI in enhancing human decision-making and the importance of understanding human biases to create more effective AI systems.

Get the full discussion here.

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

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

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

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

Check out Hugo’s latest episode with Fei-Fei Li, on How Human-Centered AI Actually Gets Built

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

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

• Computational cognitive science seeks to understand intelligence mathematically.
• Bayesian statistics is crucial for understanding human cognition.
• Inductive biases help explain how humans learn from limited data.
• Eliciting prior distributions can reveal implicit beliefs.
• The wisdom of individuals can provide richer insights than averaging group responses.
• Generative AI can mimic human cognitive processes.
• Human intelligence is shaped by constraints of data, computation, and communication.
• AI systems operate under different constraints than human cognition. Human intelligence differs fundamentally from machine intelligence.
• Generative AI can complement and enhance human learning.
• AI systems currently lack intrinsic human compatibility.
• Language training in AI helps align its understanding with human perspectives.
• Reinforcement learning from human feedback can lead to misalignment of AI goals.
• Representational alignment can improve AI's understanding of human concepts.
• AI can help humans make better decisions by providing relevant information.
• Research should focus on solving problems rather than just methods.

Chapters:

00:00 Understanding Computational Cognitive Science

13:52 Bayesian Models and Human Cognition

29:50 Eliciting Implicit Prior Distributions

38:07 The Relationship Between Human and AI Intelligence

45:15 Aligning Human and Machine Preferences

50:26 Innovations in AI and Human Interaction

55:35 Resource Rationality in Decision Making

01:00:07 Language Learning in AI Models

01:06:04 Inductive Biases in Language Learning

01:11:55 Advice for Aspiring Cognitive Scientists

01:21:19 Future Trends in Cognitive Science 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, 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:

• Check out Hugo’s latest episode with Fei-Fei Li, on How Human-Centered AI Actually Gets Built: https://high-signal.delphina.ai/episode/fei-fei-on-how-human-centered-ai-actually-gets-built?utm_source=laplace&utm_medium=podcast&utm_campaign=feifei_launch
• Tom's profile at Princeton University: https://psychology.princeton.edu/people/tom-griffiths
• Computational Cognitive Science Lab: https://cocosci.princeton.edu/
• Tom’s Google Scholar: https://scholar.google.com/citations?user=UAwKvEsAAAAJ&hl=en
• Tom's latest book, Bayesian Models of Cognition: https://mitpress.mit.edu/9780262049412/bayesian-models-of-cognition/

Transcript

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

Today’s clip is from episode 131 of the podcast, with Luke Bornn.

Luke and Alex discuss the application of generative models in sports analytics. They emphasize the importance of Bayesian modeling to account for uncertainty and contextual variations in player data.

The discussion also covers the challenges of balancing model complexity with computational efficiency, the innovative ways to hack Bayesian models for improved performance, and the significance of understanding model fitting and discretization in statistical modeling.

Get the full discussion here.

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

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

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

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

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

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Thank you to my Patrons for making this episode possible!

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

Takeaways:

• Player tracking data revolutionized sports analytics.
• Decision-making in sports involves managing uncertainty and budget constraints.
• Luke emphasizes the importance of portfolio optimization in team management.
• Clubs with high budgets can afford inefficiencies in player acquisition.
• Statistical methods provide a probabilistic approach to player value.
• Removing human bias is crucial in sports decision-making.
• Understanding player performance distributions aids in contract decisions.
• The goal is to maximize performance value per dollar spent.
• Model validation in sports requires focusing on edge cases.
• Generative models help account for uncertainty in player performance.
• Computational efficiency is key in handling large datasets.
• A diverse skill set enhances problem-solving in sports analytics.
• Broader knowledge in data science leads to innovative solutions. 
• Integrating software engineering with statistics is crucial in sports analytics.
• Model validation often requires more work than model fitting itself.
• Understanding the context of data is essential for accurate predictions.
• Continuous learning and adaptation are essential in analytics.

Chapters:

11:58 Transition from Academia to Sports Analytics

20:44 Evolution of Sports Analytics and Data Sources

23:53 Modeling Uncertainty in Decision Making

32:05 The Role of Statistical Models in Player Evaluation

39:20 Generative Models and Bayesian Framework in Sports

46:54 Hacking Bayesian Models for Better Performance

49:55 Understanding Computational Challenges in Bayesian Inference

52:44 Exploring Different Approaches to Model Fitting

56:30 Building a Comprehensive Statistical Toolbox

01:00:37 The Importance of Data Management in Modeling

01:03:21 Iterative Model Validation and Diagnostics

01:06:53 Uncovering Insights from Sports Data

01:16:47 Emerging Trends in Sports Analytics

01:21:30 Future Directions and Personal Aspirations

Links from the show:

• Luke’s website: http://www.lukebornn.com/ 
• Luke on Linkedin: https://www.linkedin.com/in/lukebornn/
• Luke on Wharton Moneyball: https://knowledge.wharton.upenn.edu/podcast/moneyball-highlights/luke-bornn-part-owner-of-ac-milan/
• LBS #108 Modeling Sports & Extracting Player Values, with Paul Sabin: https://learnbayesstats.com/episode/108-modeling-sports-extracting-player-values-paul-sabin

Transcript

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

Today’s clip is from episode 130 of the podcast, with epidemiological modeler Adam Kucharski.

This conversation explores the critical role of patient modeling during the COVID-19 pandemic, highlighting how these models informed public health decisions and the relationship between modeling and policy.

The discussion emphasizes the need for improved communication and understanding of data among the public and policymakers.

Get the full discussion here.

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

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

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

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

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

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Thank you to my Patrons for making this episode possible!

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

Takeaways:

• Epidemiology requires a blend of mathematical and statistical understanding.
• Models are essential for informing public health decisions during epidemics.
• The COVID-19 pandemic highlighted the importance of rapid modeling.
• Misconceptions about data can lead to misunderstandings in public health.
• Effective communication is crucial for conveying complex epidemiological concepts.
• Epidemic thinking can be applied to various fields, including marketing and finance.
• Public health policies should be informed by robust modeling and data analysis.
• Automation can help streamline data analysis in epidemic response.
• Understanding the limitations of models is key to effective decision-making
• Collaboration is key in developing complex models.
• Uncertainty estimation is crucial for effective decision-making.
• AI has the potential to enhance data interpretation in epidemiology.
• Educational initiatives should focus on understanding exponential growth and lagged outcomes.
• The complexity of modern epidemics requires a deeper understanding from the public.
• Understanding the balance between perfection and practicality is essential in modeling.

Chapters:

00:00 Introduction to Epidemiological Modeling

05:16 The Role of Bayesian Methods in Epidemic Forecasting

11:29 Real-World Applications of Models in Public Health

19:07 Common Misconceptions About Epidemiological Data

27:43 Understanding the Spread of Ideas and Beliefs

32:55 Workflow and Collaboration in Epidemiological Modeling

34:51 Modeling Approaches in Epidemiology

40:04 Challenges in Model Development

45:55 Uncertainty in Epidemiological Models

48:46 The Impact of AI on Epidemiology

54:55 Educational Initiatives for Future Epidemiologists

Links from the show:

• Adam’s website: https://kucharski.substack.com/
• Adam on Google Scholar: https://scholar.google.com/citations?user=eIqfmHYAAAAJ&hl=en
• Adam on Linkedin: https://www.linkedin.com/in/adam-kucharski-1a1b0225b/
• The Rules of Contagion: Why Things Spread - and Why They Stop: https://www.amazon.co.uk/Rules-Contagion-Things-Wellcome-Collection/dp/1788160207
• Adam's next book, The Uncertain Science of Certainty: https://proof.kucharski.io/
• 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

Transcript

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

Today’s clip is from episode 129 of the podcast, with AI expert and researcher Vincent Fortuin.

This conversation delves into the intricacies of Bayesian deep learning, contrasting it with traditional deep learning and exploring its applications and challenges.

Get the full discussion at https://learnbayesstats.com/episode/129-bayesian-deep-learning-ai-for-science-vincent-fortuin

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

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

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