Materials for a workshop on developing undergraduate classes on Bayesian statistics. June 2016.
Led by Allen Downey and Sanjoy Mahajan.
Focus workshops at Olin College.
Bayesian statistics is a powerful approach to problems involving probability, to making and judging statistical arguments, and to making decisions under uncertainty. But Bayesian methods are rarely taught to undergraduates, in part because they are thought to require too much background in math and statistics.
However, as we will show in this workshop, Bayesian statistics is accessible to undergraduates with diverse preparation. It allows students to study interesting and relevant topics, such as medical testing, courtroom arguments, and polling; it develops quantitative reasoning; and it develops thinking skills with broad application.
In this workshop, we present topics and methods that we teach in two courses at Olin College: "Computational Bayesian Statistics" and "Bayesian Inference and Reasoning." We include (1) problems appropriate for students with no calculus and no programming background, (2) topics that require, motivate, and reinforce, knowledge of calculus; and (3) methods that require programming.
Workshop participants will be able to use these modules and supporting materials to develop courses for a range of audiences and learning goals. Participants should be familiar with basic probability and statistics but need no knowledge of Bayesian statistics. Our computational modules use the Python programming language, but they can be adapted to any language and are accessible to anyone with basic programming skills.
Click here to view the slides from the workshop
Here is a related talk, Learning to Love Bayesian Statistics
Click this button to run the Jupyter notebook on Binder:
Here is the web page for Computational Bayesian Statistics from Fall 2014
We will have handouts from Bayesian Inference posted soon.