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    #16 Bayesian Statistics the Fun Way, with Will Kurt

    #16 Bayesian Statistics the Fun Way, with Will Kurt

    FromLearning Bayesian Statistics

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    #16 Bayesian Statistics the Fun Way, with Will Kurt

    FromLearning Bayesian Statistics

    ratings:
    Length:
    68 minutes
    Released:
    May 21, 2020
    Format:
    Podcast episode

    Description

    A librarian, a philosopher and a statistician walk into a bar — and they can’t find anybody to talk to; nobody seems to understand what they are talking about. Nobody? No! There is someone, and this someone is Will Kurt! 
    Will Kurt is the author of ‘Bayesian Statistics the Fun Way’ and ‘Get Programming With Haskell’. Currently the lead Data Scientist for the pricing and recommendations team at Hopper, he also blogs about stats and probability at https://www.countbayesie.com (countbayesie.com).
    In this episode, he’ll tell us how a Boston librarian can become a Data Scientist and work with Bayesian models everyday. He’ll also explain the value of Bayesian inference from a philosophical standpoint, why it’s useful in the travel industry and how his latest book came into life.
    Finally, Will is also a big fan of the “mind projection fallacy”, an informal fallacy first described by physicist and Bayesian philosopher Edwin Thompson Jaynes. Does that intrigue you? Well, stay tuned, he’ll tell us more in the episode…
    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/) !
    Links from the show:
    Will's Blog: https://www.countbayesie.com (https://www.countbayesie.com)
    Will on Twitter: https://twitter.com/willkurt (https://twitter.com/willkurt)
    Bayesian Statistics the Fun Way -- Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks: https://nostarch.com/learnbayes (https://nostarch.com/learnbayes)
    Get Programming with Haskell: https://www.amazon.com/Get-Programming-Haskell-Will-Kurt/dp/1617293768 (https://www.amazon.com/Get-Programming-Haskell-Will-Kurt/dp/1617293768)
    The Mind Projection Fallacy: https://en.wikipedia.org/wiki/Mind_projection_fallacy (https://en.wikipedia.org/wiki/Mind_projection_fallacy)
    Probability Theory -- The Logic of Science by E.T. Jaynes: https://www.cambridge.org/core/books/probability-theory/9CA08E224FF30123304E6D8935CF1A99 (https://www.cambridge.org/core/books/probability-theory/9CA08E224FF30123304E6D8935CF1A99)
    Wittgenstein's Lectures on the Foundations of Mathematics: https://www.amazon.com/Wittgensteins-Lectures-Foundations-Mathematics-Cambridge/dp/0226904261 (https://www.amazon.com/Wittgensteins-Lectures-Foundations-Mathematics-Cambridge/dp/0226904261)



    This podcast uses the following third-party services for analysis:

    Podcorn - https://podcorn.com/privacy
    Released:
    May 21, 2020
    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