Probabilistic Programming and Bayesian Computing with PyMC
Bayesian statistical methods provide powerful tools for solving various data science problems. The Bayesian approach yields easy-to-interpret results and automatically accounts for uncertainty in our estimates or predictions. Although computational challenges have historically been an obstacle, especially for new users, there are now mature probabilistic programming tools that are both efficient and easy to learn. We will use the latest release of PyMC (version 5) for this tutorial, but the concepts and techniques taught can be applied to any probabilistic programming framework.
This tutorial targets practicing and aspiring data scientists and analysts who seek to incorporate Bayesian statistics and probabilistic programming into their work. It will provide new users with an overview of Bayesian statistical methods and their applicability in various situations. Learners will also gain practical experience in applying these methods using PyMC, including the specification, fitting, and validation of models using a real-world dataset.
