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

Learning Bayesian Statistics

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תוכן מסופק על ידי Alexandre Andorra. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Alexandre Andorra או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.

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The Craziest Serial Rapist In British History 200 Victims | The Case Of Reynhard Sinaga 38:44

לפני 5 weeks38:44

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38:44

The Craziest Serial Rapist In British History 200 Victims | The Case Of Reynhard Sinaga A terrible true crime story about how a quiet honors student in Indonesia became an insane maniac with over 200 victims. For more than two years he had been operating in Manchester, UK, and when he was caught, everyone was shocked! The heartbreaking case of Reynhard Sinaga! The Craziest Serial Rapist In British History | The Case Of Reynhard Sinaga | True Crime Documentary Become a supporter of this podcast: https://www.spreaker.com/podcast/full-police-interrogations-911-calls-and-true-crime-investigations-true-crime-podcast-2025--6463449/support .…

לפני שנה 11:45

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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.

155 פרקים

#Tech #Alexandre ANDORRA #Data Science #Science #Podcasting Education

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

Learning Bayesian Statistics

206 subscribers

published לפני שנה

שתפו

MP3•בית הפרקים

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.

155 פרקים

#Tech #Alexandre ANDORRA #Data Science #Science #Podcasting Education

כל הפרקים

Learning Bayesian Statistics

1
BITESIZE | Exploring Dynamic Regression Models, with David Kohns 14:34

לפני 1 day14:34

14:34

Today’s clip is from episode 134 of the podcast, with David Kohns. Alex and David discuss the future of probabilistic programming, focusing on advancements in time series modeling, model selection, and the integration of AI in prior elicitation. The discussion highlights the importance of setting appropriate priors, the challenges of computational workflows, and the potential of normalizing flows to enhance Bayesian inference. 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.…

Learning Bayesian Statistics

1
#134 Bayesian Econometrics, State Space Models & Dynamic Regression, with David Kohns 1:40:55

לפני 9 ימים1:40:55

1:40:55

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.…

Learning Bayesian Statistics

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BITESIZE | Why Your Models Might Be Wrong & How to Fix it, with Sean Pinkney & Adrian Seyboldt 17:04

לפני 15 ימים17:04

17:04

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.…

Learning Bayesian Statistics

1
#133 Making Models More Efficient & Flexible, with Sean Pinkney & Adrian Seyboldt 1:12:12

לפני 22 ימים1:12:12

1:12:12

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.…

Learning Bayesian Statistics

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BITESIZE | How AI is Redefining Human Interactions, with Tom Griffiths 22:06

לפני 29 ימים22:06

22:06

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.…

Learning Bayesian Statistics

1
#132 Bayesian Cognition and the Future of Human-AI Interaction, with Tom Griffiths 1:30:15

לפני 5 weeks1:30:15

1:30:15

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.…

Learning Bayesian Statistics

1
BITESIZE | Hacking Bayesian Models for Better Performance, with Luke Bornn 13:35

לפני 6 weeks13:35

13:35

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.…

Learning Bayesian Statistics

1
#131 Decision-Making Under High Uncertainty, with Luke Bornn 1:31:46

לפני 7 weeks1:31:46

1:31:46

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.…

Learning Bayesian Statistics

1
BITESIZE | Real-World Applications of Models in Public Health, with Adam Kucharski 16:26

לפני 8 weeks16:26

16:26

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.…

Learning Bayesian Statistics

1
#130 The Real-World Impact of Epidemiological Models, with Adam Kucharski 1:09:05

לפני 9 weeks1:09:05

1:09:05

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.…

Learning Bayesian Statistics

1
BITESIZE | The Why & How of Bayesian Deep Learning, with Vincent Fortuin 11:45

לפני 10 weeks11:45

11:45

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.…

Learning Bayesian Statistics

1
#129 Bayesian Deep Learning & AI for Science with Vincent Fortuin 1:02:43

לפני 11 weeks1:02:43

1:02:43

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 06:12 Vincent's Journey into Machine Learning 12:42 Defining Bayesian Deep Learning 17:23 Current Landscape of Bayesian Libraries 22:02 Real-World Applications of Bayesian Deep Learning 24:29 When to Use Bayesian Deep Learning 29:36 Data Efficient AI and Generative Modeling 31:59 Exploring Generative AI and Meta-Learning 34:19 Understanding Bayesian Deep Learning and Prior Knowledge 39:01 Algorithms for Bayesian Deep Learning Models 43:25 Advancements in Efficient Inference Techniques 49:35 The Future of AI Models and Reliability 52:47 Advice for Aspiring Researchers in AI 56:06 Future Projects and 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, 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.…

Learning Bayesian Statistics

1
#128 Building a Winning Data Team in Football, with Matt Penn 58:11

לפני 13 weeks58:11

58:11

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.…

Learning Bayesian Statistics

1
#127 Saving Sharks... with Python, Causal Inference and Aaron MacNeil 1:04:08

לפני 15 weeks1:04:08

1:04:08

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.…

Learning Bayesian Statistics

1
#126 MMM, CLV & Bayesian Marketing Analytics, with Will Dean 54:47

לפני 17 weeks54:47

54:47

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

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

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