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Welcome to Computational Bayesian Statistics at Olin College, Fall 2018.

The Canvas site for the class is here

The software repository is here. 

The calendar is here.

The book is here.



This class is an introduction to Bayesian statistics using computational methods.  We use Python extensively, so you should take Software Design or have comparable experience in Python.

No prior knowledge of statistics is required, but if you have previously studied classical statistics (for example, the material on the Statistics AP exam), this class will still be useful to you.

At the end of this class:
1) You should understand the laws of probability, especially Bayes's theorem, and apply them to answer questions related to probability.
2) You should be able to recognize problems can be solved using Bayesian methods, implement a solution using Python and other computational tools, interpret the results, and communicate about those results.

By working on the exercises in this class, you will continue to develop your programming skills, and you will have a chance to go deeper into the Python scientific software stack, including SciPy, NumPy, Pandas, and PyMC.

Work in this class includes readings from the book and other sources, programming exercises in class and on homeworks, in-class quizzes, and three project reports.

Instructor: Allen Downey

Meetings: Tuesday and Friday, 1:30 to 3:10, AC318.

Textbook:  Think Bayes, available in hard copy from the bookseller of your choice and also from thinkbayes.com


Subpages (2): Policies Software setup