Learning Bayesian Statistics

Alexandre Andorra
Learning Bayesian Statistics

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 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 PyMC and ArviZ. I also love 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 unlock exclusive Bayesian swag on Patreon!

  1. Building a Winning Data Team in Football, with Matt Penn

    6D AGO

    Building a Winning Data Team in Football, with Matt Penn

    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 p...

    58 min
  2. Saving Sharks... with Python, Causal Inference and Aaron MacNeil

    MAR 5

    Saving Sharks... with Python, Causal Inference and Aaron MacNeil

    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...

    1h 4m
  3. MMM, CLV & Bayesian Marketing Analytics, with Will Dean

    FEB 19

    MMM, CLV & Bayesian Marketing Analytics, with Will Dean

    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,...

    55 min
  4. Bayesian Sports Analytics & The Future of PyMC, with Chris Fonnesbeck

    FEB 5

    Bayesian Sports Analytics & The Future of PyMC, with Chris Fonnesbeck

    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...

    58 min
  5. State Space Models & Structural Time Series, with Jesse Grabowski

    JAN 22

    State Space Models & Structural Time Series, with Jesse Grabowski

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! My Intuitive Bayes Online Courses1:1 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 p...

    1h 36m
  6. BART & The Future of Bayesian Tools, with Osvaldo Martin

    JAN 10

    BART & The Future of Bayesian Tools, with Osvaldo Martin

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! My Intuitive Bayes Online Courses1:1 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...

    1h 32m
  7. Learning and Teaching in the Age of AI, with Hugo Bowne-Anderson

    12/26/2024

    Learning and Teaching in the Age of AI, with Hugo Bowne-Anderson

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! My Intuitive Bayes Online Courses1:1 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

    1h 23m
  8. Exploring Bayesian Structural Equation Modeling, with Nathaniel Forde

    12/11/2024

    Exploring Bayesian Structural Equation Modeling, with Nathaniel Forde

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! My Intuitive Bayes Online Courses1:1 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,

    1h 8m

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4.7
out of 5
64 Ratings

About

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 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 PyMC and ArviZ. I also love 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 unlock exclusive Bayesian swag on Patreon!

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