ODSC Europe 2020: Bayesian Data Science: Probabilistic Programming
This course is available only as a part of subscription plans.
This session will introduce you to the wonderful world of Bayesian data science through the lens of probabilistic programming in Python. In the first half of the tutorial, we will introduce the key concepts of probability distributions via hacker statistics, hands-on simulation, and telling stories of the data-generation processes. We will also cover the basics of joint and conditional probability, Bayes' rule, and Bayesian inference, all through hands-on coding and real-world examples. In the second half of the tutorial, we will use a series of models to build your familiarity with PyMC3, showcasing how to perform the foundational inference tasks of parameter estimation, group comparison (for example, A/B tests and hypothesis testing), and arbitrary curve regression. By the end of this tutorial, you will be equipped with a solid grounding in Bayesian inference, able to write arbitrary models, and have experienced basic model checking workflow.
Overview and Author Bio
Before you get started: Prerequisites and Resources
Bayesian Data Science: Probabilistic Programming
Hugo Bowne-Anderson
Hugo Bowne-Anderson, PhD
Head of Data Science Evangelism and Marketing | Coiled