Today’s clip is from episode 133 [https://learnbayesstats.com/episode/133-making-models-more-efficient-flexible-sean-pinkney-adrian-seyboldt] 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 [https://learnbayesstats.com/episode/133-making-models-more-efficient-flexible-sean-pinkney-adrian-seyboldt].

 * Intro to Bayes Course [https://topmate.io/alex_andorra/503302] (first 2 lessons free)
 * Advanced Regression Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free)


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch [http://twitter.com/LearnBayesStats] if you're willing to correct them.

BITESIZE | Why Your Models Might Be Wrong & How to Fix it, with Sean Pinkney & Adrian Seyboldt

Proudly sponsored by PyMC Labs [https://www.pymc-labs.io/], the Bayesian Consultancy. Book a call [https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf], or get in touch [alex.andorra@pymc-labs.io]!

 * Intro to Bayes Course [https://topmate.io/alex_andorra/503302] (first 2 lessons free)
 * Advanced Regression Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free)


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] 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,

#133 Making Models More Efficient & Flexible, with Sean Pinkney & Adrian Seyboldt

Today’s clip is from episode 132 [https://learnbayesstats.com/episode/132-bayesian-cognition-and-the-future-of-human-ai-interaction-tom-griffiths] 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 [https://learnbayesstats.com/episode/132-bayesian-cognition-and-the-future-of-human-ai-interaction-tom-griffiths].

 * Intro to Bayes Course [https://topmate.io/alex_andorra/503302] (first 2 lessons free)
 * Advanced Regression Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free)


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch [http://twitter.com/LearnBayesStats] if you're willing to correct them.

BITESIZE | How AI is Redefining Human Interactions, with Tom Griffiths

Proudly sponsored by PyMC Labs [https://www.pymc-labs.io/], the Bayesian Consultancy. Book a call [https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf], or get in touch [alex.andorra@pymc-labs.io]!

 * Intro to Bayes Course [https://topmate.io/alex_andorra/503302] (first 2 lessons free)
 * Advanced Regression Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free)


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] 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

#132 Bayesian Cognition and the Future of Human-AI Interaction, with Tom Griffiths

Today’s clip is from episode 131 [https://learnbayesstats.com/episode/131-decision-making-under-high-uncertainty-luke-bornn] 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 [https://learnbayesstats.com/episode/131-decision-making-under-high-uncertainty-luke-bornn].

 * Intro to Bayes Course [https://topmate.io/alex_andorra/503302] (first 2 lessons free)
 * Advanced Regression Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free)


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch [http://twitter.com/LearnBayesStats] if you're willing to correct them.

BITESIZE | Hacking Bayesian Models for Better Performance, with Luke Bornn

Proudly sponsored by PyMC Labs [https://www.pymc-labs.io/], the Bayesian Consultancy. Book a call [https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf], or get in touch [alex.andorra@pymc-labs.io]!

 * Intro to Bayes Course [https://topmate.io/alex_andorra/503302] (first 2 lessons free)
 * Advanced Regression Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free)


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] 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.

#131 Decision-Making Under High Uncertainty, with Luke Bornn

Today’s clip is from episode 1 [https://learnbayesstats.com/episode/129-bayesian-deep-learning-ai-for-science-vincent-fortuin]30 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 at https://learnbayesstats.com/episode/129-bayesian-deep-learning-ai-for-science-vincent-fortuin

 * Intro to Bayes Course [https://topmate.io/alex_andorra/503302] (first 2 lessons free)
 * Advanced Regression Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free)


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch [http://twitter.com/LearnBayesStats] if you're willing to correct them.

BITESIZE | Real-World Applications of Models in Public Health, with Adam Kucharski

Proudly sponsored by PyMC Labs [https://www.pymc-labs.io/], the Bayesian Consultancy. Book a call [https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf], or get in touch [alex.andorra@pymc-labs.io]!

 * Intro to Bayes Course [https://topmate.io/alex_andorra/503302] (first 2 lessons free)
 * Advanced Regression Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free)


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] 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

#130 The Real-World Impact of Epidemiological Models, with Adam Kucharski

Today’s clip is from episode 129 [https://learnbayesstats.com/episode/129-bayesian-deep-learning-ai-for-science-vincent-fortuin] 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 [https://topmate.io/alex_andorra/503302] (first 2 lessons free)
 * Advanced Regression Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free)


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch [http://twitter.com/LearnBayesStats] if you're willing to correct them.

BITESIZE | The Why & How of Bayesian Deep Learning, with Vincent Fortuin

Proudly sponsored by PyMC Labs [https://www.pymc-labs.io/], the Bayesian Consultancy. Book a call [https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf], or get in touch [alex.andorra@pymc-labs.io]!

 * Intro to Bayes Course [https://topmate.io/alex_andorra/503302] (first 2 lessons free)
 * Advanced Regression Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free)


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] 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

#129 Bayesian Deep Learning & AI for Science with Vincent Fortuin

Are you a researcher or data scientist / analyst / ninja? Do you want to learn
Bayesian inference, stay up to date or simply want to understand what Bayesian
inference is? Then this podcast is for you! You'll hear from researchers and
practitioners of all fields about how they use Bayesian statistics, and how in
turn YOU can apply these methods in your modeling workflow. When I started
learning Bayesian methods, I really wished there were a podcast out there that
could introduce me to the methods, the projects and the people who make all that
possible. So I created "Learning Bayesian Statistics", where you'll get to hear
how Bayesian statistics are used to detect black matter in outer space, forecast
elections or understand how diseases spread and can ultimately be stopped. But
this show is not only about successes -- it's also about failures, because
that's how we learn best. So you'll often hear the guests talking about what
*didn't* work in their projects, why, and how they overcame these challenges.
Because, in the end, we're all lifelong learners! My name is Alex Andorra by the
way, and I live in Paris. By day, I'm a data scientist and modeler at the
https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, I don't (yet) fight
crime, but I'm an open-source enthusiast and core contributor to the python
packages https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/
(ArviZ). I also love https://www.pollsposition.com/ (election forecasting) and,
most importantly, Nutella. But I don't like talking about it – I prefer eating
it. So, whether you want to learn Bayesian statistics or hear about the latest
libraries, books and applications, this podcast is for you -- just subscribe!
You can also support the show and https://www.patreon.com/learnbayesstats
(unlock exclusive Bayesian swag on Patreon)! This podcast uses the following
third-party services for analysis: Podcorn - https://podcorn.com/privacy

Alexandre ANDORRA

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