Discover this podcast and so much more

Podcasts are free to enjoy without a subscription. We also offer ebooks, audiobooks, and so much more for just $11.99/month.

#2 When should you use Bayesian tools, and Bayes in sports analytics, with Chris Fonnesbeck

#2 When should you use Bayesian tools, and Bayes in sports analytics, with Chris Fonnesbeck

FromLearning Bayesian Statistics


#2 When should you use Bayesian tools, and Bayes in sports analytics, with Chris Fonnesbeck

FromLearning Bayesian Statistics

ratings:
Length:
44 minutes
Released:
Oct 23, 2019
Format:
Podcast episode

Description

When are Bayesian methods most useful? Conversely, when should you NOT use them? How do you teach them? What are the most important skills to pick-up when learning Bayes? And what are the most difficult topics, the ones you should maybe save for later?
In this episode, you’ll hear Chris Fonnesbeck answer these questions from the perspective of marine biology and sports analytics. Chris is indeed the New York Yankees’ senior quantitative analyst and an associate professor at Vanderbilt University School of Medicine. 
He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis. He also created PyMC, a library to do probabilistic programming in python, and is the author of several tutorials at PyCon and PyData conferences.
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:
Chris on Twitter: https://twitter.com/fonnesbeck (https://twitter.com/fonnesbeck)
PyMC3, Probabilistic Programming in Python: https://docs.pymc.io/ (https://docs.pymc.io/)
Chris on GitHub: https://github.com/fonnesbeck (https://github.com/fonnesbeck)
An introduction to Markov Chain Monte Carlo using PyMC3 - PyData London 2019: https://www.youtube.com/watch?v=SS_pqgFziAg (https://www.youtube.com/watch?v=SS_pqgFziAg)
Introduction to Statistical Modeling with Python - PyCon 2017 - video: https://www.youtube.com/watch?v=TMmSESkhRtI (https://www.youtube.com/watch?v=TMmSESkhRtI)
Introduction to Statistical Modeling with Python - PyCon 2017 - code repo: https://github.com/fonnesbeck/intro_stat_modeling_2017 (https://github.com/fonnesbeck/intro_stat_modeling_2017)
Bayesian Non-parametric Models for Data Science using PyMC3 - PyCon 2018: https://www.youtube.com/watch?v=-sIOMs4MSuA (https://www.youtube.com/watch?v=-sIOMs4MSuA)
Statistical Data Analysis in Python: https://github.com/fonnesbeck/statistical-analysis-python-tutorial (https://github.com/fonnesbeck/statistical-analysis-python-tutorial)



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

Podcorn - https://podcorn.com/privacy
Released:
Oct 23, 2019
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