Last night I made my triumphant return for the 21st meeting, where I presented a talk I called "Bayesian Zig Zag". Here's the abstract:
Tools like PyMC and Stan make it easy to implement probabilistic models, but getting started can be challenging. In this talk, I present a strategy for simultaneously developing and implementing probabilistic models by alternating between forward and inverse probabilities and between grid algorithms and MCMC. This process helps developers validate modeling decisions and verify their implementation.
As an example, I will use a version of the "Boston Bruins problem", which I presented in Think Bayes, updated for the 2017-18 season. I will also present and request comments on my plans for the second edition of Think Bayes.When I wrote the abstract, I was confident that the Bruins would be in the Stanley Cup final, but that is not how it worked out. I adapted, using results from the first two games of the NHL final series to generate predictions for the next game.
Here are the slides from the talk:
And here is the Jupyter notebook I presented. If you want to follow along, you'll see that there is a slide that introduces each section of the notebook, and then you can read the details. If you have a Python installation with PyMC, you can download the notebook from the repository and try it out.
The talk starts with basic material that should be accessible for beginners, and ends with a hierarchical Bayesian model of a Poisson process, so it covers a lot of ground! I hope you find it useful.
For people who were there, thank you for coming (all the way from Australia!), and thanks for the questions, comments, and conversation. Thanks again to Jordi and Colin for organizing, to WeWork for hosting, and to QuantumBlack for sponsoring.