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...

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

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...