This is the web site for tutorials on Bayesian statistics led by Allen Downey.


An introduction to Bayesian statistics using Python.  Bayesian statistics are usually presented mathematically, but many of the ideas are easier to understand computationally.  People who know Python can get started quickly and use Bayesian analysis to solve real problems.  This tutorial is based on material and case studies from Think Bayes (O’Reilly Media).


Bayesian statistical methods are becoming more common and more important, 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.

I will present simple programs that demonstrate the concepts of Bayesian statistics, and apply them to a range of example problems.  Participants will work hands-on with example code and practice on example problems.

Attendees should have at least basic level Python and basic statistics.  If you learned about Bayes’s theorem and probability distributions at some time, that’s enough, even if you don’t remember it!

Attendees should bring a laptop with Python and matplotlib.  You can work in any environment; you just need to be able to download a Python program and run it.  I will provide code to help attendees get set up ahead of time.


Allen Downey is a Professor of Computer Science at Olin College and author of Think Python, Think Stats, Think Bayes, and Think Complexity. He holds B.S. and M.S. degrees from MIT and a Ph.D. in Computer Science from U.C. Berkeley. He has previously taught at Colby College and Wellesley College, and was a Visiting Scientist at Google, Inc.