
86 episodes

Learning Bayesian Statistics Learn Bayes Stats
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- Technology
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4.8 • 56 Ratings
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Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is?
Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow.
When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible.
So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped.
But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners!
My name is Alex Andorra by the way, and I live in Paris. 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!
This podcast uses the following third-party services for analysis:
Podcorn - https://podcorn.com/privacy
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Multilevel Regression, Post-Stratification & Electoral Dynamics, with Tarmo Jüristo
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
My Intuitive Bayes Online Courses1:1 Mentorship with me
One of the greatest features of this podcast, and my work in general, is that I keep getting surprised. Along the way, I keep learning, and I meet fascinating people, like Tarmo Jüristo.
Tarmo is hard to describe. These days, he’s heading an NGO called Salk, in the Baltic state called Estonia. Among other things, they are studying and forecasting elections, which is how we met and ended up collaborating with PyMC Labs, our Bayesian consultancy.
But Tarmo is much more than that. Born in 1971 in what was still the Soviet Union, he graduated in finance from Tartu University. He worked in finance and investment banking until the 2009 crisis, when he quit and started a doctorate in… cultural studies. He then went on to write for theater and TV, teaching literature, anthropology and philosophy. An avid world traveler, he also teaches kendo and Brazilian jiu-jitsu.
As you’ll hear in the episode, after lots of adventures, he established Salk, and they just used a Bayesian hierarchical model with post-stratification to forecast the results of the 2023 Estonian parliamentary elections and target the campaign efforts to specific demographics.
Oh, and let thing: Tarmo is a fan of the show — I told he was a great guy ;)
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, Nathaniel Neitzke, 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, Raul Maldonado, 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, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh and Grant Pezzolesi.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
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Sequential Monte Carlo & Bayesian Computation Algorithms, with Nicolas Chopin
But other methods exist to infer the posterior distributions of your models — like Sequential Monte Carlo (SMC), INLA, Variational Bayes. Let's dive into those in this episode!
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Neuroscience of Perception: Exploring the Brain, with Alan Stocker
Did you know that the way your brain perceives speed depends on your priors? And it’s not the same at night? And it’s not the same for everybody?
This is another of these episodes I love where we dive into neuroscience, how the brain works, and how it relates to Bayesian stats. -
Bayesian Additive Regression Trees (BARTs), with Sameer Deshpande
In this episode, we’ll go to the roots of regression trees. Our tree expert will be no one else than Sameer Deshpande. Sameer is an assistant professor of Statistics at the University of Wisconsin-Madison. Prior to that, he completed a postdoc at MIT and earned his Ph.D. in Statistics from UPenn.
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Decision-Making & Cost Effectiveness Analysis for Health Economics, with Gianluca Baio
Decision-making and cost effectiveness analyses rarely get as important as in the health systems — where matters of life and death are not a metaphor. Bayesian statistical modeling is extremely helpful in this field, with its ability to quantify uncertainty, include domain knowledge, and incorporate causal reasoning.
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Exploring MCMC Sampler Algorithms, with Matt D. Hoffman
You’ll hear about the circumstances Matt would advise picking up Bayesian stats, generalized HMC, blocked samplers, why do the samplers he works on have food-based names, etc.
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Coolest show around
Super inspiring discussions with awesome tips and real life experience !
Cant wait for the next episode to come out 🔥