We'd like you to have some basic familiarity with probability before starting the course. Most good material on this is in textbooks rather than online (at least as far as we have seen). So if you are not familiar with basic probability, conditional probability, Bayes theorem, probability distributions and probability density functions, expectations/variances, we'd like you to spend some time going through this material. You don't need to spend a lot of time doing mathematical problems, but you should try to understand the basic concepts and try to get some intuition for the main ideas. If you have questions, we'll be setting up a website that you can post questions to in advance of the course and one of the instructors will respond.

We recommend either of:

• Bolstad, Introduction to Bayesian Statistics (chapters 4, 5, and 7)
  
• Kruschke, Doing Bayesian Data Analysis (chapter 3, and Section 4.1)
  

If you don't have access to these through your library, introductory probability and statistics textbooks have the same information in more detail. One possibility is to use one of the textbooks listed below but to use Amazon's 'Search Inside the Book' feature to see the table of contents and a selection of pages from either Bolstad or Kruschke in order to see what material is covered, and at what level, in the Bolstad or Kruschke listed above.

Helpful Textbooks (older editions will be fine):

• Rice, Mathematical Statistics and Data Analysis (chapters 1, 2, 4)
  
• Pitman, Probability (chapters 1, 3, 4)
  
• DeGroot and Schervish, Probability and Statistics (chapters 1-4) (or earlier editions by DeGroot alone)
  
• Ross, A First Course in Probability (chapters 2-5)

If you're interested in a bit of pre-course exposure to Bayesian statistics (not required), you might check out Chapters 2, 4, 5, and 7 of the Kruschke book.