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#57 Forecasting French Elections, with… Mystery Guest

#57 Forecasting French Elections, with… Mystery Guest

FromLearning Bayesian Statistics


#57 Forecasting French Elections, with… Mystery Guest

FromLearning Bayesian Statistics

ratings:
Length:
82 minutes
Released:
Mar 3, 2022
Format:
Podcast episode

Description

No, no, don't leave! You did not click on the wrong button. You are indeed on Alex Andorra’s podcast. The podcast that took the Bayesian world by a storm: “Learning Bayesian Statistics”, and that Barack Obama deemed “the best podcast in the whole galaxy” – or maybe Alex said that, I don’t remember.
Alex made us discover new methods, new ideas, and mostly new people. But what do we really know about him? Does he even really exist? To find this out I put on my Frenchest beret, a baguette under my arm, and went undercover to try to find him.
And I did ! So today for a special episode I, https://www.learnbayesstats.com/episode/44-bayesian-models-at-scale-remi-louf (Rémi Louf), will be the one asking questions and making bad jokes with a French accent.
Before letting him in, here’s what I got on him so far.
By day, Alex is a Bayesian modeler at the https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, he doesn’t (yet) fight crime but he’s an open-source enthusiast and core contributor to https://docs.pymc.io/en/v3/ (PyMC) and https://arviz-devs.github.io/ (ArviZ).
An always-learning statistician, Alex loves building models and https://github.com/pollsposition/models (studying elections) and human behavior.
When he’s not working, he loves hiking, exercising, meditating and reading nerdy books and novels. He also loves chocolate a bit too much, but he doesn’t like talking about it – he prefers eating it.
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (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, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, 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, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland and Aubrey Clayton.
Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
Links from the show:
Alex on Twitter: https://twitter.com/alex_andorra (https://twitter.com/alex_andorra)
Alex on GitHub: https://github.com/AlexAndorra (https://github.com/AlexAndorra)
Alex on LinkedIn: https://www.linkedin.com/in/aandorra-pollsposition/ (https://www.linkedin.com/in/aandorra-pollsposition/)
Intuitive Bayes Introductory Course: https://www.intuitivebayes.com/ (https://www.intuitivebayes.com/)
PyMC Labs consultancy: https://www.pymc-labs.io/ (https://www.pymc-labs.io/)
PollsPosition GitHub repository: https://github.com/pollsposition (https://github.com/pollsposition)
French Presidents' popularity dashboard: https://www.pollsposition.com/popularity (https://www.pollsposition.com/popularity)
Learning Bayesian Statistics YouTube channel: https://www.youtube.com/channel/UCAwVseuhVrpJFfik_cMHrhQ (https://www.youtube.com/channel/UCAwVseuhVrpJFfik_cMHrhQ)
Love the podcast? Leave a review on Podchaser: https://www.podchaser.com/podcasts/learning-bayesian-statistics-932588 (https://www.podchaser.com/podcasts/learning-bayesian-statistics-932588)



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Released:
Mar 3, 2022
Format:
Podcast episode

Titles in the series (100)

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 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)! This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy