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PyCon 2013: Bayesian Statistics Made Simple

An intermediate-level tutorial 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 some Python have a head start. We will use material from Downey's books Think Stats: Probability and Statistics for Programmers (O’Reilly Media), and Think Bayes, a work in progress at http://thinkbayes.com. Part of PyCon 2013, the largest annual gathering for the community using and developing the open-source Python programming language.
When 13 March 2013
from 01:30 PM to 04:30 PM
Where Santa Clara CA USA
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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.

Students should have at least basic 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!

Students should bring a laptop with Python 2.x and matplotlib.  You can work in any environment; you just need to be able to download a Python program and run it.

Outline: 1. Bayes’s theorem. 2. Representing probability distributions. 3. Bayesian estimation. 4. Biased coins and student test scores. 5. Censored data. 6. The locomotive / German tank problem. 7. Hierarchical models and the hidden species problem.

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