scipy2021

Title

Bayesian Data Science by Simulation

Abstract

This introduces tutorial participants on how to use a probabilistic programming language, PyMC3, to perform a variety of statistical inference tasks. We will use hands-on instruction working on real-world examples (which have been simplified for pedagogical purposes) to show you how to do parameter estimation and inference, with a specific focus on building towards generalized Bayesian A/B/C/D/E… testing, a.k.a. multi-group experimental comparison, hierarchical modelling, and arbitrary curve regression.

Keywords

bayesian
simulation
data science

Additional Tutorial Information

The tutorial material is available at https://github.com/ericmjl/bayesian-stats-modelling-tutorial.

Tutorial Prerequisites

Tutorial participants should be familiar with the NumPy API, as well as matplotlib for plotting. They should feel comfortable operating in a Jupyter environment. Binder is available as a backup compute option for participants.