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Learning Bayesian Statistics
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A tartalmat a Alexandre Andorra biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Alexandre Andorra vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
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)!
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141 epizódok
Mind megjelölése nem lejátszottként
Manage series 2635823
A tartalmat a Alexandre Andorra biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Alexandre Andorra vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
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)!
…
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141 epizódok
Minden epizód
×Proudly sponsored by PyMC Labs , the Bayesian Consultancy. Book a call , or get in touch ! Intro to Bayes Course (first 2 lessons free) Advanced Regression Course (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Visit our Patreon page to unlock exclusive Bayesian swag ;) Takeaways: Marketing analytics is crucial for understanding customer behavior. PyMC Marketing offers tools for customer lifetime value analysis. Media mix modeling helps allocate marketing spend effectively. Customer Lifetime Value (CLV) models are essential for understanding long-term customer behavior. Productionizing models is essential for real-world applications. Productionizing models involves challenges like model artifact storage and version control. MLflow integration enhances model tracking and management. The open-source community fosters collaboration and innovation. Understanding time series is vital in marketing analytics. Continuous learning is key in the evolving field of data science. Chapters : 00:00 Introduction to Will Dean and His Work 10:48 Diving into PyMC Marketing 17:10 Understanding Media Mix Modeling 25:54 Challenges in Productionizing Models 35:27 Exploring Customer Lifetime Value Models 44:10 Learning and Development in Data Science Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia and Michael Cao . Links from the show: Get your ticket for Field of Play: https://www.fieldofplay.co.uk/tickets Will's website: https://wd60622.github.io/blog/ Will on GitHub: https://github.com/wd60622/ Will on Linkedin: https://www.linkedin.com/in/williambdean/ PyMC-Marketing: https://www.pymc-marketing.io/en/stable/ Transcript This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.…
Proudly sponsored by PyMC Labs , the Bayesian Consultancy. Book a call , or get in touch ! Intro to Bayes Course (first 2 lessons free) Advanced Regression Course (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Visit our Patreon page to unlock exclusive Bayesian swag ;) Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire and Mike Loncaric. Takeaways: The evolution of sports modeling is tied to the availability of high-frequency data. Bayesian methods are valuable in handling messy, hierarchical data. Communication between data scientists and decision-makers is crucial for effective model use. Models are often wrong, and learning from mistakes is part of the process. Simplicity in models can sometimes yield better results than complexity. The integration of analytics in sports is still developing, with opportunities in various sports. Transparency in research and development teams enhances decision-making. Understanding uncertainty in models is essential for informed decisions. The balance between point estimates and full distributions is a challenge. Iterative model development is key to improving analytics in sports. It's important to avoid falling in love with a single model. Data simulation can validate model structures before real data is used. Gaussian processes offer flexibility in modeling without strict functional forms. Structural time series help separate projection from observation noise. Transitioning from sports analytics to consulting opens new opportunities. Continuous learning is essential in the field of statistics. The demand for Bayesian methods is growing across various industries. Community-driven projects can lead to innovative solutions. Chapters : 03:07 The Evolution of Modeling in Sports Analytics 06:03 Transitioning from Academia to Sports Modeling 08:56 The Role of Bayesian Methods in Sports Analytics 11:49 Communicating Models and Insights to Decision Makers 15:12 Learning from Mistakes in Model Development 18:06 The Importance of Model Flexibility and Iteration 21:02 Utilizing Simulation for Model Validation 23:50 Choosing the Right Model Structure for Data 27:04 Starting with Simple Models and Building Complexity 29:29 Advancements in Gaussian Processes and PyMC 31:54 Exploring Structural Time Series and GPs 37:34 Transitioning to PyMC Labs and New Opportunities 42:40 Innovations in Variational Inference Methods 48:50 Future Vision for PyMC and Community Engagement 50:43 Surprises in Bayesian Methods Adoption 54:08 Reflections on Problem Solving and Influential Figures Links from the show: Alex's and Chris’ GP tutorial at PyData NYC: https://youtu.be/u6I5pN_Q6r4?si=5IzrQB_0k30Rmzhu Chris on GitHub: https://github.com/fonnesbeck Chris on Linkedin: https://www.linkedin.com/in/christopher-fonnesbeck-374a492a/ Chris on Blue Sky: https://bsky.app/profile/fonnesbeck.bsky.social Developing Hierarchical Models for Sports Analytics: https://www.pymc-labs.com/blog-posts/2023-09-15-Hierarchical-models-Chris-Fonnesbeck/ Beyond Moneyball: Phillies Data Scientist Give Students a Real-World Look at How Today’s MLB Teams Use Data: https://datascience.virginia.edu/news/beyond-moneyball-phillies-data-scientist-give-students-real-world-look-how-todays-mlb-teams HSGP Reference & First Steps: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Basic.html HSGP Advanced Usage: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Advanced.html Data simulation with PyMC: https://tomicapretto.com/posts/2024-11-01_pymc-data-simulation/ LBS #124 State Space Models & Structural Time Series, with Jesse Grabowski: https://learnbayesstats.com/episode/124-state-space-models-structural-time-series-jesse-grabowski Transcript This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.…
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1 #124 State Space Models & Structural Time Series, with Jesse Grabowski 1:35:43
1:35:43
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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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1 #123 BART & The Future of Bayesian Tools, with Osvaldo Martin 1:32:13
1:32:13
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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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1 #122 Learning and Teaching in the Age of AI, with Hugo Bowne-Anderson 1:23:10
1:23:10
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Proudly sponsored by PyMC Labs , the Bayesian Consultancy. Book a call , or get in touch ! My Intuitive Bayes Online Courses 1:1 Mentorship with me Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Visit our Patreon page to unlock exclusive Bayesian swag ;) Takeaways : Effective data science education requires feedback and rapid iteration. Building LLM applications presents unique challenges and opportunities. The software development lifecycle for AI differs from traditional methods. Collaboration between data scientists and software engineers is crucial. Hugo's new course focuses on practical applications of LLMs. Continuous learning is essential in the fast-evolving tech landscape. Engaging learners through practical exercises enhances education. POC purgatory refers to the challenges faced in deploying LLM-powered software. Focusing on first principles can help overcome integration issues in AI. Aspiring data scientists should prioritize problem-solving over specific tools. Engagement with different parts of an organization is crucial for data scientists. Quick paths to value generation can help gain buy-in for data projects. Multimodal models are an exciting trend in AI development. Probabilistic programming has potential for future growth in data science. Continuous learning and curiosity are vital in the evolving field of data science. Chapters : 09:13 Hugo's Journey in Data Science and Education 14:57 The Appeal of Bayesian Statistics 19:36 Learning and Teaching in Data Science 24:53 Key Ingredients for Effective Data Science Education 28:44 Podcasting Journey and Insights 36:10 Building LLM Applications: Course Overview 42:08 Navigating the Software Development Lifecycle 48:06 Overcoming Proof of Concept Purgatory 55:35 Guidance for Aspiring Data Scientists 01:03:25 Exciting Trends in Data Science and AI 01:10:51 Balancing Multiple Roles in Data Science 01:15:23 Envisioning Accessible Data Science for All Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire and Mike Loncaric. Links from the show : Alex's last paper, “Unveiling True Talent: The Soccer Factor Model for Skill Evaluation”: https://arxiv.org/abs/2412.05911 Associated code and data: https://github.com/AlexAndorra/football-modeling/tree/main/40_submissions/MIT_Sloan_2025/01_Paper Hugo on Blue Sky: https://bsky.app/profile/hugobowne.bsky.social Hugo’s website: https://hugobowne.github.io/ Hugo on Linkedin: https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/ Hugo on GitHub: https://github.com/hugobowne Vanishing Gradients podcast: https://vanishinggradients.fireside.fm/hosts/hugobowne High Signal podcast: https://high-signal.delphina.ai/ 25% discount on Hugo’s course on Building LLM Applications: https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=LEARNBAYES25 Lightning lessons if people want to get a sense of Hugo's teaching style and content: https://maven.com/p/38a781/building-with-gen-ai-from-first-principles?utm_medium=ll_share_link&utm_source=instructor https://maven.com/p/a6f9bf/mastering-llm-application-testing?utm_medium=ll_share_link&utm_source=instructor Transcript : This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.…
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1 #121 Exploring Bayesian Structural Equation Modeling, with Nathaniel Forde 1:08:13
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Proudly sponsored by PyMC Labs , the Bayesian Consultancy. Book a call , or get in touch ! My Intuitive Bayes Online Courses 1:1 Mentorship with me Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Visit our Patreon page to unlock exclusive Bayesian swag ;) Takeaways : CFA is commonly used in psychometrics to validate theoretical constructs. Theoretical structure is crucial in confirmatory factor analysis. Bayesian approaches offer flexibility in modeling complex relationships. Model validation involves both global and local fit measures. Sensitivity analysis is vital in Bayesian modeling to avoid skewed results. Complex models should be justified by their ability to answer specific questions. The choice of model complexity should balance fit and theoretical relevance. Fitting models to real data builds confidence in their validity. Divergences in model fitting indicate potential issues with model specification. Factor analysis can help clarify causal relationships between variables. Survey data is a valuable resource for understanding complex phenomena. Philosophical training enhances logical reasoning in data science. Causal inference is increasingly recognized in industry applications. Effective communication is essential for data scientists. Understanding confounding is crucial for accurate modeling. Chapters : 10:11 Understanding Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) 20:11 Application of SEM and CFA in HR Analytics 30:10 Challenges and Advantages of Bayesian Approaches in SEM and CFA 33:58 Evaluating Bayesian Models 39:50 Challenges in Model Building 44:15 Causal Relationships in SEM and CFA 49:01 Practical Applications of SEM and CFA 51:47 Influence of Philosophy on Data Science 54:51 Designing Models with Confounding in Mind 57:39 Future Trends in Causal Inference 01:00:03 Advice for Aspiring Data Scientists 01:02:48 Future Research Directions Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström and Stefan. Links from the show: Modeling Webinar – Bayesian Causal Inference & Propensity Scores: https://www.youtube.com/watch?v=y9BeOr0AETw&list=PL7RjIaSLWh5lDvhGf6qs_im0fRzOeFN5_&index=9 LBS #102, Bayesian Structural Equation Modeling & Causal Inference in Psychometrics, with Ed Merkle: https://learnbayesstats.com/episode/102-bayesian-structural-equation-modeling-causal-inference-psychometrics-ed-merkle/ Nate’s website: https://nathanielf.github.io/ Nate on GitHub: https://github.com/NathanielF Nate on Linkedin: https://www.linkedin.com/in/nathaniel-forde-2477a265/ Nate on Twitter: https://x.com/forde_nathaniel Confirmatory Factor Analysis and Structural Equation Models in Psychometrics: https://www.pymc.io/projects/examples/en/latest/case_studies/CFA_SEM.html Measurement, Latent Factors and the Garden of Forking Paths: https://nathanielf.github.io/posts/post-with-code/CFA_AND_SEM/CFA_AND_SEM.html Bayesian Non-parametric Causal Inference: https://www.pymc.io/projects/examples/en/latest/causal_inference/bayesian_nonparametric_causal.html Simpson’s paradox: https://www.pymc.io/projects/examples/en/latest/causal_inference/GLM-simpsons-paradox.html Transcript: This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.…
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1 #120 Innovations in Infectious Disease Modeling, with Liza Semenova & Chris Wymant 1:01:39
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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 ;) ------------------------- Love the insights from this episode? Make sure you never miss a beat with Chatpods ! Whether you're commuting, working out, or just on the go, Chatpods lets you capture and summarize key takeaways effortlessly. Save time, stay organized, and keep your thoughts at your fingertips. Download Chatpods directly from App Store or Google Play and use it to listen to this podcast today! https://www.chatpods.com/?fr=LearningBayesianStatistics ------------------------- Takeaways : Epidemiology focuses on health at various scales, while biology often looks at micro-level details. Bayesian statistics helps connect models to data and quantify uncertainty. Recent advancements in data collection have improved the quality of epidemiological research. Collaboration between domain experts and statisticians is essential for effective research. The COVID-19 pandemic has led to increased data availability and international cooperation. Modeling infectious diseases requires understanding complex dynamics and statistical methods. Challenges in coding and communication between disciplines can hinder progress. Innovations in machine learning and neural networks are shaping the future of epidemiology. The importance of understanding the context and limitations of data in research. Chapters : 00:00 Introduction to Bayesian Statistics and Epidemiology 03:35 Guest Backgrounds and Their Journey 10:04 Understanding Computational Biology vs. Epidemiology 16:11 The Role of Bayesian Statistics in Epidemiology 21:40 Recent Projects and Applications in Epidemiology 31:30 Sampling Challenges in Health Surveys 34:22 Model Development and Computational Challenges 36:43 Navigating Different Jargons in Survey Design 39:35 Post-COVID Trends in Epidemiology 42:49 Funding and Data Availability in Epidemiology 45:05 Collaboration Across Disciplines 48:21 Using Neural Networks in Bayesian Modeling 51:42 Model Diagnostics in Epidemiology 55:38 Parameter Estimation in Compartmental Models Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström and Stefan. Links from the show: LBS #21, Gaussian Processes, Bayesian Neural Nets & SIR Models, with Elizaveta Semenova: https://learnbayesstats.com/episode/21-gaussian-processes-bayesian-neural-nets-sir-models-with-elizaveta-semenova/ Liza’s website: https://www.elizaveta-semenova.com/ Liza on GitHub: https://github.com/elizavetasemenova Liza on LinkedIn: https://www.linkedin.com/in/elizaveta-semenova/ Liza on Google Scholar: https://scholar.google.com/citations?user=jqGIgFEAAAAJ&hl=en Chris' page: https://www.bdi.ox.ac.uk/Team/c-wymant Chris on GitHub: https://github.com/chrishiv Chris on LinkedIn: https://www.linkedin.com/in/chris-wymant-65661274/ Chris on Blue Sky: https://bsky.app/profile/chriswymant.bsky.social Chris on Google Scholar: https://scholar.google.com/citations?user=OJ6t2UwAAAAJ&hl=en PriorVAE Paper : Explains how to build an emulator for a GP using a deep generative model (Variational Autoencoder, or VAE) and apply it within MCMC. Link to the paper PriorCVAE Paper : Builds on PriorVAE by encoding model parameters along with emulating stochastic process realisations. Includes examples for GPs, ODEs, and double-well models. Link to the paper StanCon 2024 Tutorial : A tutorial covering the basics of sequential decision-making, with a demo of Bayesian Optimization using Stan. Link to the tutorial Numpyro Course : Materials from a course Liza taught -- great for learning Numpyro. Link to the course aggVAE Paper : An application of PriorVAE to the problem of changing boundaries. Link to the paper Transcript This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.…
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1 #119 Causal Inference, Fiction Writing and Career Changes, with Robert Kubinec 1:25:01
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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 : Bob's research focuses on corruption and political economy. Measuring corruption is challenging due to the unobservable nature of the behavior. The challenge of studying corruption lies in obtaining honest data. Innovative survey techniques, like randomized response, can help gather sensitive data. Non-traditional backgrounds can enhance statistical research perspectives. Bayesian methods are particularly useful for estimating latent variables. Bayesian methods shine in situations with prior information. Expert surveys can help estimate uncertain outcomes effectively. Bob's novel, 'The Bayesian Hitman,' explores academia through a fictional lens. Writing fiction can enhance academic writing skills and creativity. The importance of community in statistics is emphasized, especially in the Stan community. Real-time online surveys could revolutionize data collection in social science. Chapters : 00:00 Introduction to Bayesian Statistics and Bob Kubinec 06:01 Bob's Academic Journey and Research Focus 12:40 Measuring Corruption: Challenges and Methods 18:54 Transition from Government to Academia 26:41 The Influence of Non-Traditional Backgrounds in Statistics 34:51 Bayesian Methods in Political Science Research 42:08 Bayesian Methods in COVID Measurement 51:12 The Journey of Writing a Novel 01:00:24 The Intersection of Fiction and Academia Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström and Stefan. Links from the show: Robert’s website (includes blog posts): https://www.robertkubinec.com/ Robert on GitHub: https://github.com/saudiwin Robert on Linkedin: https://www.linkedin.com/in/robert-kubinec-9191a9a/ Robert on Google Scholar: https://scholar.google.com/citations?user=bhOaXR4AAAAJ&hl=en Robert on Twitter: https://x.com/rmkubinec Robert on Bluesky: https://bsky.app/profile/rmkubinec.bsky.social The Bayesian Hitman: https://www.amazon.com/Bayesian-Hitman-Robert-M-Kubinec/dp/B0D6M4WNRZ/ Ordbetareg overview: https://www.robertkubinec.com/ordbetareg Idealstan – this isn’t out yet, but you can access an older working paper here: https://osf.io/preprints/osf/8j2bt Ordinal Regression tutorial, Michael Betancourt: https://betanalpha.github.io/assets/case_studies/ordinal_regression.html Andrew Heiss blog: https://www.andrewheiss.com/blog/ Transcript This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.…
Proudly sponsored by PyMC Labs , the Bayesian Consultancy. Book a call , or get in touch ! My Intuitive Bayes Online Courses 1:1 Mentorship with me Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Visit our Patreon page to unlock exclusive Bayesian swag ;) Takeaways : User experience is crucial for the adoption of Stan. Recent innovations include adding tuples to the Stan language, new features and improved error messages. Tuples allow for more efficient data handling in Stan. Beginners often struggle with the compiled nature of Stan. Improving error messages is crucial for user experience. BridgeStan allows for integration with other programming languages and makes it very easy for people to use Stan models. Community engagement is vital for the development of Stan. New samplers are being developed to enhance performance. The future of Stan includes more user-friendly features. Chapters : 00:00 Introduction to the Live Episode 02:55 Meet the Stan Core Developers 05:47 Brian Ward's Journey into Bayesian Statistics 09:10 Charles Margossian's Contributions to Stan 11:49 Recent Projects and Innovations in Stan 15:07 User-Friendly Features and Enhancements 18:11 Understanding Tuples and Their Importance 21:06 Challenges for Beginners in Stan 24:08 Pedagogical Approaches to Bayesian Statistics 30:54 Optimizing Monte Carlo Estimators 32:24 Reimagining Stan's Structure 34:21 The Promise of Automatic Reparameterization 35:49 Exploring BridgeStan 40:29 The Future of Samplers in Stan 43:45 Evaluating New Algorithms 47:01 Specific Algorithms for Unique Problems 50:00 Understanding Model Performance 54:21 The Impact of Stan on Bayesian Research 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 and Robert Flannery. Links from the show: Come see the show live at PyData NYC: https://pydata.org/nyc2024/ LBS #90, Demystifying MCMC & Variational Inference, with Charles Margossian: https://learnbayesstats.com/episode/90-demystifying-mcmc-variational-inference-charles-margossian/ Charles' website: https://charlesm93.github.io/ Charles on GitHub: https://github.com/charlesm93 Charles on LinkedIn: https://www.linkedin.com/in/charles-margossian-3428935b/ Charles on Google Scholar: https://scholar.google.com/citations?user=nPtLsvIAAAAJ&hl=en Charles on Twitter: https://x.com/charlesm993 Brian's website: https://brianward.dev/ Brian on GitHub: https://github.com/WardBrian Brian on LinkedIn: https://www.linkedin.com/in/ward-brianm/ Brian on Google Scholar: https://scholar.google.com/citations?user=bzosqW0AAAAJ&hl=en Brian on Twitter: https://x.com/ward_brianm Bob Carpenter's reflections on StanCon: https://statmodeling.stat.columbia.edu/category/bayesian-statistics/ 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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1 #117 Unveiling the Power of Bayesian Experimental Design, with Desi Ivanova 1:13:12
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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 : Designing experiments is about optimal data gathering. The optimal design maximizes the amount of information. The best experiment reduces uncertainty the most. Computational challenges limit the feasibility of BED in practice. Amortized Bayesian inference can speed up computations. A good underlying model is crucial for effective BED. Adaptive experiments are more complex than static ones. The future of BED is promising with advancements in AI. Chapters : 00:00 Introduction to Bayesian Experimental Design 07:51 Understanding Bayesian Experimental Design 19:58 Computational Challenges in Bayesian Experimental Design 28:47 Innovations in Bayesian Experimental Design 40:43 Practical Applications of Bayesian Experimental Design 52:12 Future of Bayesian Experimental Design 01:01:17 Real-World Applications and Impact 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 and Gary Clarke. Links from the show: Come see the show live at PyData NYC: https://pydata.org/nyc2024/ Desi’s website: https://desirivanova.com/ Desi on GitHub: https://github.com/desi-ivanova Desi on Google Scholar: https://scholar.google.com/citations?user=AmX6sMIAAAAJ&hl=en Desi on Linkedin: https://www.linkedin.com/in/dr-ivanova/ Desi on Twitter: https://x.com/desirivanova LBS #34, Multilevel Regression, Post-stratification & Missing Data, with Lauren Kennedy: https://learnbayesstats.com/episode/34-multilevel-regression-post-stratification-missing-data-lauren-kennedy/ LBS #35, The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner/ LBS #45, Biostats & Clinical Trial Design, with Frank Harrell: https://learnbayesstats.com/episode/45-biostats-clinical-trial-design-frank-harrell/ 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/ Bayesian Experimental Design (BED) with BayesFlow and PyTorch: https://github.com/stefanradev93/BayesFlow/blob/dev/examples/michaelis_menten_BED_tutorial.ipynb Paper – Modern Bayesian Experimental Design: https://arxiv.org/abs/2302.14545 Paper – Optimal experimental design; Formulations and computations: https://arxiv.org/pdf/2407.16212 Information theory, inference and learning algorithms , by the great late Sir David MacKay: https://www.inference.org.uk/itprnn/book.pdf Patterns, Predictions and Actions , Moritz Hard and Ben Recht https://mlstory.org/index.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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1 #116 Mastering Soccer Analytics, with Ravi Ramineni 1:32:46
1:32:46
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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 : Building an athlete management system and a scouting and recruitment platform are key goals in football analytics. The focus is on informing training decisions, preventing injuries, and making smart player signings. Avoiding false positives in player evaluations is crucial, and data analysis plays a significant role in making informed decisions. There are similarities between different football teams, and the sport has social and emotional aspects. Transitioning from on-premises SQL servers to cloud-based systems is a significant endeavor in football analytics. Analytics is a tool that aids the decision-making process and helps mitigate biases. The impact of analytics in soccer can be seen in the decline of long-range shots. Collaboration and trust between analysts and decision-makers are crucial for successful implementation of analytics. The limitations of available data in football analytics hinder the ability to directly measure decision-making on the field. Analyzing the impact of coaches in sports analytics is challenging due to the difficulty of separating their effect from other factors. Current data limitations make it hard to evaluate coaching performance accurately. Predictive metrics and modeling play a crucial role in soccer analytics, especially in predicting the career progression of young players. Improving tracking data and expanding its availability will be a significant focus in the future of soccer analytics. Chapters : 00:00 Introduction to Ravi and His Role at Seattle Sounders 06:30 Building an Analytics Department 15:00 The Impact of Analytics on Player Recruitment and Performance 28:00 Challenges and Innovations in Soccer Analytics 42:00 Player Health, Injury Prevention, and Training 55:00 The Evolution of Data-Driven Strategies 01:10:00 Future of Analytics in Sports 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 and Gary Clarke. Links from the show: LBS Sports Analytics playlist: https://www.youtube.com/playlist?list=PL7RjIaSLWh5kDiPVMUSyhvFaXL3NoXOe4 Ravi on Linkedin: https://www.linkedin.com/in/ravi-ramineni-3798374/ Ravi on Twitter: https://x.com/analyseFooty Decisions in Football - The Power of Compounding | StatsBomb Conference 2023: https://www.youtube.com/watch?v=D7CXtwDg9lM The Signal and the Noise: https://www.amazon.com/Signal-Noise-Many-Predictions-Fail-but/dp/0143125087 PreliZ – A tool-box for prior elicitation: https://preliz.readthedocs.io/en/latest/ Ravi talking on Ted Knutson's podcast: https://open.spotify.com/episode/1exLBfyFf0d1dm2IaXkd2v More about Ravi's work at the Seattle Sounders: https://www.trumedianetworks.com/expected-value-podcast/ravi-ramineni 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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1 #115 Using Time Series to Estimate Uncertainty, with Nate Haines 1:39:51
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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 : State space models and traditional time series models are well-suited to forecast loss ratios in the insurance industry, although actuaries have been slow to adopt modern statistical methods. Working with limited data is a challenge, but informed priors and hierarchical models can help improve the modeling process. Bayesian model stacking allows for blending together different model predictions and taking the best of both (or all if more than 2 models) worlds. Model comparison is done using out-of-sample performance metrics, such as the expected log point-wise predictive density (ELPD). Brute leave-future-out cross-validation is often used due to the time-series nature of the data. Stacking or averaging models are trained on out-of-sample performance metrics to determine the weights for blending the predictions. Model stacking can be a powerful approach for combining predictions from candidate models. Hierarchical stacking in particular is useful when weights are assumed to vary according to covariates. BayesBlend is a Python package developed by Ledger Investing that simplifies the implementation of stacking models, including pseudo Bayesian model averaging, stacking, and hierarchical stacking. Evaluating the performance of patient time series models requires considering multiple metrics, including log likelihood-based metrics like ELPD, as well as more absolute metrics like RMSE and mean absolute error. Using robust variants of metrics like ELPD can help address issues with extreme outliers. For example, t-distribution estimators of ELPD as opposed to sample sum/mean estimators. It is important to evaluate model performance from different perspectives and consider the trade-offs between different metrics. Evaluating models based solely on traditional metrics can limit understanding and trust in the model. Consider additional factors such as interpretability, maintainability, and productionization. Simulation-based calibration (SBC) is a valuable tool for assessing parameter estimation and model correctness. It allows for the interpretation of model parameters and the identification of coding errors. In industries like insurance, where regulations may restrict model choices, classical statistical approaches still play a significant role. However, there is potential for Bayesian methods and generative AI in certain areas. Chapters : 00:00 Introduction to Bayesian Modeling in Insurance 13:00 Time Series Models and Their Applications 30:51 Bayesian Model Averaging Explained 56:20 Impact of External Factors on Forecasting 01:25:03 Future of Bayesian Modeling 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 and Francesco Madrisotti . Links from the show: Nate’s website: http://haines-lab.com/ Nate on GitHub: https://github.com/Nathaniel-Haines Nate on Linkedin: https://www.linkedin.com/in/nathaniel-haines-216049101/ Nate on Twitter: https://x.com/nate__haines Nate on Google Scholar: https://scholar.google.com/citations?user=lg741SgAAAAJ LBS #14 Hidden Markov Models & Statistical Ecology, with Vianey Leos-Barajas: https://learnbayesstats.com/episode/14-hidden-markov-models-statistical-ecology-with-vianey-leos-barajas/ 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 #109 Prior Sensitivity Analysis, Overfitting & Model Selection, with Sonja Winter: https://learnbayesstats.com/episode/109-prior-sensitivity-analysis-overfitting-model-selection-sonja-winter/ BayesBlend – Easy Model Blending: https://arxiv.org/abs/2405.00158 BayesBlend documentation: https://ledger-investing-bayesblend.readthedocs-hosted.com/en/latest/ SBC paper: https://arxiv.org/abs/1804.06788 Isaac Asimov’s Foundation (Hari Seldon): https://en.wikipedia.org/wiki/Hari_Seldon Stancon 2023 talk on Ledger’s Bayesian modeling workflow: https://github.com/stan-dev/stancon2023/blob/main/Nathaniel-Haines/slides.pdf Ledger’s Bayesian modeling workflow: https://arxiv.org/abs/2407.14666v1 More on Ledger Investing: https://www.ledgerinvesting.com/about-us Transcript This is an automatic transcript and may therefore contain errors. 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1 #114 From the Field to the Lab – A Journey in Baseball Science, with Jacob Buffa 1:01:32
1:01:32
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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 : Education and visual communication are key in helping athletes understand the impact of nutrition on performance. Bayesian statistics are used to analyze player performance and injury risk. Integrating diverse data sources is a challenge but can provide valuable insights. Understanding the specific needs and characteristics of athletes is crucial in conditioning and injury prevention. The application of Bayesian statistics in baseball science requires experts in Bayesian methods. Traditional statistical methods taught in sports science programs are limited. Communicating complex statistical concepts, such as Bayesian analysis, to coaches and players is crucial. Conveying uncertainties and limitations of the models is essential for effective utilization. Emerging trends in baseball science include the use of biomechanical information and computer vision algorithms. Improving player performance and injury prevention are key goals for the future of baseball science. Chapters : 00:00 The Role of Nutrition and Conditioning 05:46 Analyzing Player Performance and Managing Injury Risks 12:13 Educating Athletes on Dietary Choices 18:02 Emerging Trends in Baseball Science 29:49 Hierarchical Models and Player Analysis 36:03 Challenges of Working with Limited Data 39:49 Effective Communication of Statistical Concepts 47:59 Future Trends: Biomechanical Data Analysis and Computer Vision Algorithms 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 and Francesco Madrisotti . Links from the show : LBS Sports Analytics playlist: https://www.youtube.com/playlist?list=PL7RjIaSLWh5kDiPVMUSyhvFaXL3NoXOe4 Jacob on Linkedin: https://www.linkedin.com/in/jacob-buffa-46bb7481/ Jacob on Twitter: https://x.com/EBA_Buffa The Book – Playing The Percentages In Baseball: https://www.amazon.com/Book-Playing-Percentages-Baseball/dp/1494260174 Future Value – The Battle for Baseball's Soul and How Teams Will Find the Next Superstar: https://www.amazon.com/Future-Value-Battle-Baseballs-Superstar/dp/1629377678 The MVP Machine – How Baseball's New Nonconformists Are Using Data to Build Better Players: https://www.amazon.com/MVP-Machine-Baseballs-Nonconformists-Players/dp/1541698940 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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1 #113 A Deep Dive into Bayesian Stats, with Alex Andorra, ft. the Super Data Science Podcast 1:30:51
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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 is a powerful framework for handling complex problems, making use of prior knowledge, and excelling with limited data. Bayesian statistics provides a framework for updating beliefs and making predictions based on prior knowledge and observed data. Bayesian methods allow for the explicit incorporation of prior assumptions, which can provide structure and improve the reliability of the analysis. There are several Bayesian frameworks available, such as PyMC, Stan, and Bambi, each with its own strengths and features. PyMC is a powerful library for Bayesian modeling that allows for flexible and efficient computation. For beginners, it is recommended to start with introductory courses or resources that provide a step-by-step approach to learning Bayesian statistics. PyTensor leverages GPU acceleration and complex graph optimizations to improve the performance and scalability of Bayesian models. ArviZ is a library for post-modeling workflows in Bayesian statistics, providing tools for model diagnostics and result visualization. Gaussian processes are versatile non-parametric models that can be used for spatial and temporal data analysis in Bayesian statistics. Chapters : 00:00 Introduction to Bayesian Statistics 07:32 Advantages of Bayesian Methods 16:22 Incorporating Priors in Models 23:26 Modeling Causal Relationships 30:03 Introduction to PyMC, Stan, and Bambi 34:30 Choosing the Right Bayesian Framework 39:20 Getting Started with Bayesian Statistics 44:39 Understanding Bayesian Statistics and PyMC 49:01 Leveraging PyTensor for Improved Performance and Scalability 01:02:37 Exploring Post-Modeling Workflows with ArviZ 01:08:30 The Power of Gaussian Processes in Bayesian Modeling 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 and Francesco Madrisotti . Links from the show: Original episode on the Super Data Science podcast: https://www.superdatascience.com/podcast/bayesian-methods-and-applications-with-alexandre-andorra Advanced Regression with Bambi and PyMC: https://www.intuitivebayes.com/advanced-regression Gaussian Processes: HSGP Reference & First Steps: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Basic.html Modeling Webinar – Fast & Efficient Gaussian Processes: https://www.youtube.com/watch?v=9tDMouGue8g Modeling spatial data with Gaussian processes in PyMC: https://www.pymc-labs.com/blog-posts/spatial-gaussian-process-01/ Hierarchical Bayesian Modeling of Survey Data with Post-stratification: https://www.pymc-labs.com/blog-posts/2022-12-08-Salk/ PyMC docs: https://www.pymc.io/welcome.html Bambi docs: https://bambinos.github.io/bambi/ PyMC Labs: https://www.pymc-labs.com/ 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/ LBS #63 Media Mix Models & Bayes for Marketing, with Luciano Paz: https://learnbayesstats.com/episode/63-media-mix-models-bayes-marketing-luciano-paz/ LBS #83 Multilevel Regression, Post-Stratification & Electoral Dynamics, with Tarmo Jüristo: https://learnbayesstats.com/episode/83-multilevel-regression-post-stratification-electoral-dynamics-tarmo-juristo/ Jon Krohn on YouTube: https://www.youtube.com/JonKrohnLearns Jon Krohn on Linkedin: https://www.linkedin.com/in/jonkrohn/ Jon Krohn on Twitter: https://x.com/JonKrohnLearns Transcript This is an automatic transcript and may therefore contain errors. 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1 #112 Advanced Bayesian Regression, with Tomi Capretto 1:27:19
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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 : Teaching Bayesian Concepts Using M&Ms: Tomi Capretto uses an engaging classroom exercise involving M&Ms to teach Bayesian statistics, making abstract concepts tangible and intuitive for students. Practical Applications of Bayesian Methods: Discussion on the real-world application of Bayesian methods in projects at PyMC Labs and in university settings, emphasizing the practical impact and accessibility of Bayesian statistics. Contributions to Open-Source Software: Tomi’s involvement in developing Bambi and other open-source tools demonstrates the importance of community contributions to advancing statistical software. Challenges in Statistical Education: Tomi talks about the challenges and rewards of teaching complex statistical concepts to students who are accustomed to frequentist approaches, highlighting the shift to thinking probabilistically in Bayesian frameworks. Future of Bayesian Tools: The discussion also touches on the future enhancements for Bambi and PyMC, aiming to make these tools more robust and user-friendly for a wider audience, including those who are not professional statisticians. Chapters : 05:36 Tomi's Work and Teaching 10:28 Teaching Complex Statistical Concepts with Practical Exercises 23:17 Making Bayesian Modeling Accessible in Python 38:46 Advanced Regression with Bambi 41:14 The Power of Linear Regression 42:45 Exploring Advanced Regression Techniques 44:11 Regression Models and Dot Products 45:37 Advanced Concepts in Regression 46:36 Diagnosing and Handling Overdispersion 47:35 Parameter Identifiability and Overparameterization 50:29 Visualizations and Course Highlights 51:30 Exploring Niche and Advanced Concepts 56:56 The Power of Zero-Sum Normal 59:59 The Value of Exercises and Community 01:01:56 Optimizing Computation with Sparse Matrices 01:13:37 Avoiding MCMC and Exploring Alternatives 01:18:27 Making Connections Between Different Models Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan and Francesco Madrisotti . Links from the show: Tomi’s website: https://tomicapretto.com/ Tomi on GitHub: https://github.com/tomicapretto Tomi on Linkedin: https://www.linkedin.com/in/tom%C3%A1s-capretto-a89873106/ Tomi on Twitter: https://x.com/caprettotomas Advanced Regression course (get 10% off if you’re a Patron of the show): https://www.intuitivebayes.com/advanced-regression Bambi: https://bambinos.github.io/bambi/ LBS #35 The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner/ LBS #1 Bayes, open-source and bioinformatics, with Osvaldo Martin: https://learnbayesstats.com/episode/1-bayes-open-source-and-bioinformatics-with-osvaldo-martin/ patsy - Describing statistical models in Python: https://patsy.readthedocs.io/en/latest/ formulae - Formulas for mixed-models in Python: https://bambinos.github.io/formulae/ Introducing Bayesian Analysis With m&m's®: An Active-Learning Exercise for Undergraduates: https://www.tandfonline.com/doi/full/10.1080/10691898.2019.1604106 Richly Parameterized Linear Models Additive, Time Series, and Spatial Models Using Random Effects https://www.routledge.com/Richly-Parameterized-Linear-Models-Additive-Time-Series-and-Spatial-Models-Using-Random-Effects/Hodges/p/book/9780367533731 Dan Simpson’s Blog (link to blogs with the ‘sparse matrices’ tag): https://dansblog.netlify.app/#category=Sparse%20matrices Repository for Sparse Matrix-Vector dot product: https://github.com/tomicapretto/dot_tests 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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