Bayesian statistics made simple with Alan Downey

Alan Downey, author of one of my most favourite python books, "Python for Software Design: How to Think Like a Computer Scientist" did a tutorial session t a tutorial on Bayesian statistics and here is the web page he created for the tutorial. This tutorial is an introduction to Bayesian statistics using Python.

 

Following is the outline to this 3 hour long lecture from Allen's website
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.
Students 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! Students should be comfortable with logarithms and plotting data on a log scale.
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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