Learning Bayesian Statistics podcast

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محتوای ارائه شده توسط Alexandre Andorra. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Alexandre Andorra یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

Learning Bayesian Statistics

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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 Estonia. 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)!

152 قسمت

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

Learning Bayesian Statistics

206 subscribers

updated ۷ روز پیش

اشتراک گذاری

خانه سری•Feed

152 قسمت

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

همه قسمت ها

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

۷ روز پیش1:12:12

پخش در آینده

لیست ها

پسندیدن

دوست داشته شد

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.…

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

14 روز پیش22:06

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.…

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

22 روز پیش1:30:15

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.…

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

28 روز پیش13:35

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.…

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

۵ weeks پیش1:31:46

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.…

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

۶ weeks پیش16:26

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.…

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

۷ weeks پیش1:09:05

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.…

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

۸ weeks پیش11:45

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.…

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

۹ weeks پیش1:02:43

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.…

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

11 weeks پیش58:11

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.…

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

13 weeks پیش1:04:08

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.…

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

15 weeks پیش54:47

54:47

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#125 Bayesian Sports Analytics & The Future of PyMC, with Chris Fonnesbeck 58:15

17 weeks پیش58:15

58:15

Proudly sponsored by PyMC Labs , the Bayesian Consultancy. Book a call , or get in touch ! Intro to Bayes Course (first 2 lessons free) Advanced Regression Course (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Visit our Patreon page to unlock exclusive Bayesian swag ;) Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire and Mike Loncaric. Takeaways: The evolution of sports modeling is tied to the availability of high-frequency data. Bayesian methods are valuable in handling messy, hierarchical data. Communication between data scientists and decision-makers is crucial for effective model use. Models are often wrong, and learning from mistakes is part of the process. Simplicity in models can sometimes yield better results than complexity. The integration of analytics in sports is still developing, with opportunities in various sports. Transparency in research and development teams enhances decision-making. Understanding uncertainty in models is essential for informed decisions. The balance between point estimates and full distributions is a challenge. Iterative model development is key to improving analytics in sports. It's important to avoid falling in love with a single model. Data simulation can validate model structures before real data is used. Gaussian processes offer flexibility in modeling without strict functional forms. Structural time series help separate projection from observation noise. Transitioning from sports analytics to consulting opens new opportunities. Continuous learning is essential in the field of statistics. The demand for Bayesian methods is growing across various industries. Community-driven projects can lead to innovative solutions. Chapters : 03:07 The Evolution of Modeling in Sports Analytics 06:03 Transitioning from Academia to Sports Modeling 08:56 The Role of Bayesian Methods in Sports Analytics 11:49 Communicating Models and Insights to Decision Makers 15:12 Learning from Mistakes in Model Development 18:06 The Importance of Model Flexibility and Iteration 21:02 Utilizing Simulation for Model Validation 23:50 Choosing the Right Model Structure for Data 27:04 Starting with Simple Models and Building Complexity 29:29 Advancements in Gaussian Processes and PyMC 31:54 Exploring Structural Time Series and GPs 37:34 Transitioning to PyMC Labs and New Opportunities 42:40 Innovations in Variational Inference Methods 48:50 Future Vision for PyMC and Community Engagement 50:43 Surprises in Bayesian Methods Adoption 54:08 Reflections on Problem Solving and Influential Figures Links from the show: Alex's and Chris’ GP tutorial at PyData NYC: https://youtu.be/u6I5pN_Q6r4?si=5IzrQB_0k30Rmzhu Chris on GitHub: https://github.com/fonnesbeck Chris on Linkedin: https://www.linkedin.com/in/christopher-fonnesbeck-374a492a/ Chris on Blue Sky: https://bsky.app/profile/fonnesbeck.bsky.social Developing Hierarchical Models for Sports Analytics: https://www.pymc-labs.com/blog-posts/2023-09-15-Hierarchical-models-Chris-Fonnesbeck/ Beyond Moneyball: Phillies Data Scientist Give Students a Real-World Look at How Today’s MLB Teams Use Data: https://datascience.virginia.edu/news/beyond-moneyball-phillies-data-scientist-give-students-real-world-look-how-todays-mlb-teams HSGP Reference & First Steps: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Basic.html HSGP Advanced Usage: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Advanced.html Data simulation with PyMC: https://tomicapretto.com/posts/2024-11-01_pymc-data-simulation/ LBS #124 State Space Models & Structural Time Series, with Jesse Grabowski: https://learnbayesstats.com/episode/124-state-space-models-structural-time-series-jesse-grabowski Transcript This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.…

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#124 State Space Models & Structural Time Series, with Jesse Grabowski 1:35:43

19 weeks پیش1:35:43

1:35:43

Proudly sponsored by PyMC Labs , the Bayesian Consultancy. Book a call , or get in touch ! My Intuitive Bayes Online Courses 1:1 Mentorship with me Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Visit our Patreon page to unlock exclusive Bayesian swag ;) Takeaways: Bayesian statistics offers a robust framework for econometric modeling. State space models provide a comprehensive way to understand time series data. Gaussian random walks serve as a foundational model in time series analysis. Innovations represent external shocks that can significantly impact forecasts. Understanding the assumptions behind models is key to effective forecasting. Complex models are not always better; simplicity can be powerful. Forecasting requires careful consideration of potential disruptions. Understanding observed and hidden states is crucial in modeling. Latent abilities can be modeled as Gaussian random walks. State space models can be highly flexible and diverse. Composability allows for the integration of different model components. Trends in time series should reflect real-world dynamics. Seasonality can be captured through Fourier bases. AR components help model residuals in time series data. Exogenous regression components can enhance state space models. Causal analysis in time series often involves interventions and counterfactuals. Time-varying regression allows for dynamic relationships between variables. Kalman filters were originally developed for tracking rockets in space. The Kalman filter iteratively updates beliefs based on new data. Missing data can be treated as hidden states in the Kalman filter framework. The Kalman filter is a practical application of Bayes' theorem in a sequential context. Understanding the dynamics of systems is crucial for effective modeling. The state space module in PyMC simplifies complex time series modeling tasks. Chapters : 00:00 Introduction to Jesse Krabowski and Time Series Analysis 04:33 Jesse's Journey into Bayesian Statistics 10:51 Exploring State Space Models 18:28 Understanding State Space Models and Their Components 40:39 Composability of State Space Models 48:36 Understanding Trends and Derivatives 52:35 The Importance of Seasonality in Time Series 56:41 Components of Time Series Analysis 01:00:46 Exogenous Regression in State Space Models 01:06:41 Impulse Response Functions and Causality 01:11:30 Why Kalman Filter Is So Powerful 01:24:28 Future Directions and Applications Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire and Mike Loncaric. Links from the show: Jesse on GitHub: https://github.com/jessegrabowski Jesse on LinkedIn: www.linkedin.com/in/jessegrabowski Jesse on Google Scholar: https://scholar.google.com/citations?user=vOCjGPwAAAAJ&hl=en State space presentation repo: https://github.com/jessegrabowski/statespace-presentation/tree/main Try the statespace module on pymc-experimental: https://github.com/pymc-devs/pymc-experimental Durbin, James, and Siem Jan Koopman. Time series analysis by state space methods , Oxford, 2012: https://academic.oup.com/book/16563?login=false Hyndman, Rob and George Athanasopoulos, Forecasting: Principals and Practice, 3rd Edition . Otexts, 2018: https://otexts.com/fpp3/ Roger Labbe, Kalman and Bayesian Filters in Python : https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python Quantecon.org: https://quantecon.org/ Lecture on Kalman Filtering: https://python.quantecon.org/kalman.html Mamba – Linear-Time Sequence Modeling with State Spaces (state spaces in machine learning): https://arxiv.org/abs/2312.00752 Paper explanation: https://www.youtube.com/watch?v=9dSkvxS2EB0 Good lecture on the statistics of the Kalman filter: https://www.youtube.com/watch?v=8lPBkkbtNW8 And on structural state space models: https://www.youtube.com/watch?v=2vf-d_fRCXs Transcript This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.…

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#123 BART & The Future of Bayesian Tools, with Osvaldo Martin 1:32:13

21 weeks پیش1:32:13

1:32:13

Proudly sponsored by PyMC Labs , the Bayesian Consultancy. Book a call , or get in touch ! My Intuitive Bayes Online Courses 1:1 Mentorship with me Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Visit our Patreon page to unlock exclusive Bayesian swag ;) Takeaways: BART models are non-parametric Bayesian models that approximate functions by summing trees. BART is recommended for quick modeling without extensive domain knowledge. PyMC-BART allows mixing BART models with various likelihoods and other models. Variable importance can be easily interpreted using BART models. PreliZ aims to provide better tools for prior elicitation in Bayesian statistics. The integration of BART with Bambi could enhance exploratory modeling. Teaching Bayesian statistics involves practical problem-solving approaches. Future developments in PyMC-BART include significant speed improvements. Prior predictive distributions can aid in understanding model behavior. Interactive learning tools can enhance understanding of statistical concepts. Integrating PreliZ with PyMC improves workflow transparency. Arviz 1.0 is being completely rewritten for better usability. Prior elicitation is crucial in Bayesian modeling. Point intervals and forest plots are effective for visualizing complex data. Chapters : 00:00 Introduction to Osvaldo Martin and Bayesian Statistics 08:12 Exploring Bayesian Additive Regression Trees (BART) 18:45 Prior Elicitation and the PreliZ Package 29:56 Teaching Bayesian Statistics and Future Directions 45:59 Exploring Prior Predictive Distributions 52:08 Interactive Modeling with PreliZ 54:06 The Evolution of ArviZ 01:01:23 Advancements in ArviZ 1.0 01:06:20 Educational Initiatives in Bayesian Statistics 01:12:33 The Future of Bayesian Methods Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire and Mike Loncaric. Links from the show: LBS #1 Bayes, open-source and bioinformatics, with Osvaldo Martin: https://learnbayesstats.com/episode/1-bayes-open-source-and-bioinformatics-with-osvaldo-martin/ LBS #58 Bayesian Modeling and Computation, with Osvaldo Martin, Ravin Kumar and Junpeng Lao: https://learnbayesstats.com/episode/58-bayesian-modeling-computation-osvaldo-martin-ravin-kumar-junpeng-lao/ LBS #112 Advanced Bayesian Regression, with Tomi Capretto: https://learnbayesstats.com/episode/112-advanced-bayesian-regression-tomi-capretto/ Osvaldo's website: https://aloctavodia.github.io/ Osvaldo on GitHub: https://github.com/aloctavodia Osvaldo on LinkedIn: https://www.linkedin.com/in/osvaldo-martin-447a662b1/ Osvaldo on Google Scholar: https://scholar.google.com/citations?user=WUvDNnkAAAAJ Osvaldo on Mastodon: https://bayes.club/@aloctavodia Osvaldo on BlueSky: https://bsky.app/profile/aloctavodia.bsky.social PyMC-BART package: https://www.pymc.io/projects/bart/en/latest/index.html PyMC-BART paper: https://arxiv.org/abs/2206.03619 PreliZ for prior elicitation: https://preliz.readthedocs.io/en/latest/ Prior Knowledge Elicitation: The Past, Present, and Future: https://projecteuclid.org/journals/bayesian-analysis/advance-publication/Prior-Knowledge-Elicitation-The-Past-Present-and-Future/10.1214/23-BA1381.full ArviZ 1.0 repository: https://arviz-plots.readthedocs.io/en/latest/ Practical MCMC course: https://www.intuitivebayes.com/practical-mcmc Cohort Retention Analysis with BART: https://juanitorduz.github.io/retention_bart/ HSGP Reference & First Steps: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Basic.html HSGP Advanced Usage: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Advanced.html Transcript This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.…

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