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 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)!
Latest episodes
Feb 21, 2024 • 55min
#100 Reactive Message Passing & Automated Inference in Julia, with Dmitry Bagaev
Dmitry Bagaev discusses reactive message passing in Bayesian inference and the development of RxInfer.jl. He talks about the challenges and benefits, variational inference, and the trade-offs in architecture. Dmitry shares insights into his startup Lazy Dynamics and the future of automated Bayesian inference. Also, his background in Russia, extreme sports hobbies, and the user-friendliness of inference methods are discussed.
Feb 16, 2024 • 10min
The biggest misconceptions about Bayes & Quantum Physics
The podcast explores common misconceptions in quantum physics and Bayesian probability, dispelling biases. It also delves into the concept of subjective reality and the significance of context in comprehending information.
Feb 14, 2024 • 11min
Why would you use Bayesian Statistics?
In this podcast, quantum physics expert Chris Ferrie explores the link between quantum physics and Bayesian statistics. They discuss the practical application of Bayesian statistics, the challenges faced in transitioning to the subjective interpretation of probability, and the benefits of building something from scratch to deepen understanding. Ferrie also shares insights on using Bayesian statistics in research and the usefulness of tools like Q infer for solving problems in quantum physics.
Feb 9, 2024 • 1h 8min
#99 Exploring Quantum Physics with Bayesian Stats, with Chris Ferrie
In this episode, Chris Ferrie, an Associate Professor at the Centre for Quantum Software and Information of the University of Technology Sydney, discusses the utility of Bayesian stats in quantum physics research and shares insights from his work as an author. They also talk about science communication, education, and a shocking revelation about Ant Man.
Feb 5, 2024 • 9min
How do sampling algorithms scale?
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meListen to the full episode: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/Watch the interview: https://www.youtube.com/watch?v=vVqZ0WWXX7g Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !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 and Cory Kiser.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Feb 4, 2024 • 9min
Why choose new algorithms instead of HMC?
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meListen to the full episode: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/Watch the interview: https://www.youtube.com/watch?v=vVqZ0WWXX7g Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !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 and Cory Kiser.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Jan 24, 2024 • 1h 5min
#98 Fusing Statistical Physics, Machine Learning & Adaptive MCMC, with Marylou Gabrié
Marylou Gabrié, assistant professor at CMAP, Ecole Polytechnique in Paris, discusses the fusion of statistical physics and machine learning. Topics include machine learning for scientific computing, adaptive Monte Carlo with normalizing flows, sampling discrete parameters in generative models, and machine learning in scientific computing.
Jan 20, 2024 • 10min
Why Even Care About Science & Rationality
Learn how to interpret data analysis with skepticism and make informed decisions based on evidence and reason. Explore the importance of making data-based decisions and examples of Simpson's paradox. Discover the impact of causal graphs and Bayesian reasoning on decision-making, including the base rate fallacy and interpreting medical test results.
Jan 17, 2024 • 10min
How To Get Into Causal Inference
Dive into the fascinating world of causal inference, where Bayesian concepts merge seamlessly with interactive learning. Discover the significant contributions of Judea Pearl and unravel the complexities of collider bias in epidemiology. Explore the low birth weight paradox, revealing a surprising twist in maternal smoking outcomes. This discussion promises to illuminate how causal diagrams clarify sampling biases and deepen understanding of these intricate statistical relationships.
7 snips
Jan 9, 2024 • 1h 13min
#97 Probably Overthinking Statistical Paradoxes, with Allen Downey
Guest Allen Downey, renowned author in programming and data science, discusses statistical paradoxes, Bayesian thinking, and the misconception that Bayesian and frequentist methods yield the same results. They also explore causal inference, overfitting regression models, sampling biases, and the practical application of Bayesian methods in decision making. The chapter ends with a discussion on ensuring a habitable planet and improving the quality of life by 2100.
