Learning Bayesian Statistics With Pokemon GO by Tushar Chandra

Talk Abstract

In the mobile game Pokemon GO, players can rarely encounter “shiny” Pokemon. The exact appearance rates are unknown. But by using Bayesian inference and PyMC3, we can model different species’ shiny rates. In this beginner-level tutorial, we will introduce fundamental principles at the heart of Bayesian modeling; then we will apply them to develop PyMC3 models that can answer questions about Pokemon GO.

Talk

Tushar Chandra

Tushar is a senior data scientist at Nielsen Global Media in Chicago. At Nielsen, he works on developing Bayesian models for next-generation audience measurement. He loves cats (living with two, Luna and Ruby), chai, and college football. This is his first conference talk!


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8 Likes

Looking forward to answering your questions about this! Some resources:

I also put together some Resources to learn about Bayesian inference as I was learning for the first time, and brought it back and updated it for this talk. Let me know if you find it useful!

4 Likes

Really good talk!
One note about divergences: only HMC/NUTS can detect/report when they diverge, so that’s why you didn’t get divergences with Slice. The traces look fine though :+1:

3 Likes

Thanks for the note! The Slice sampler was added at the last minute when I was like ah, oh no, why is this model not working, so I appreciate the insight into the details! :slight_smile:

Thank you for the talk - really enjoyed it. Accessible for a complete beginner.

2 Likes

Really cool talk +1, also I love the live coding feel to it

2 Likes

Amazing talk! Love the live coding and intuitive explanations.

1 Like

Thank you all! I appreciate it, I’m glad you liked the talk :slight_smile: