Bayesian Statistics Made Simple
Bayesian Statistics Made Simple

Abstract: 

Bayesian statistical methods are becoming more common, but there are not many resources to help beginners get started. People who know Python can use their programming skills to get a head start. In this workshop, I introduce Bayesian methods using grid algorithms, which are a simple way to get started, yet powerful enough to solve many real-world problems.
As the primary example, we will use data to estimate proportions and use the results to solve the ""Bayesian Bandit"" problem. This example is meant to be fun, but it is also useful; the same methods apply to A/B testing and Bayesian medical trials.

Session Outline
1. Introduction
2. Bayes's Theorem and the Cookie problem
3. Bayesian inference and the Euro problem
4. Bayesian decision analysis and the Bandit problem
5. More resources

Background Knowledge
Basic Python

Bio: 

Allen Downey is a Professor of Computer Science at Olin College of Engineering in Needham, MA. He is the author of several books related to computer science and data science, including Think Python, Think Stats, Think Bayes, and Think Complexity. Prof Downey has taught at Colby College and Wellesley College, and in 2009 he was a Visiting Scientist at Google. He received his Ph.D. in Computer Science from U.C. Berkeley, and M.S. and B.S. degrees from MIT.