Jan. 4: Brief organizational meeting.

Jan. 6: Ch. 1 text. If time permits, additional introductory remarks drawn from short-course slides.

Jan. 11: Ch. 2 text. Check the resources page for the slides I wanted to show, plus links concerning the Sally Clark case.

Jan. 13: Ch. 3 text.

Jan. 18: Interval estimation (deferred from Ch. 3), then on to Ch. 4

Jan. 20 (No pre-lecture e-mail required.) Further Ch. 4 discussion (slides). Then in the way of review (and some preview), plus seeing a slightly more complex example, we'll look at Module 1 from this short-course (that we didn't get to on Jan. 6).

Jan. 25: Ch. 5 text. There is a snippet of R code for an example we'll discuss.

Jan. 27: We will continue on with our discussion of Ch. 5. Particularly we will use the mean-square error example there as a segue into decision theory. This short handout may be of help.

Feb. 1: Ch. 6 text. Here is my "animated" R demo for Gibbs sampling.

Feb. 3: Back to decision theory discussion. There are some slides with gaps that we will fill in.

Feb. 8: More Ch. 6 discussion.

Feb. 10: Expected utility, plus some wrap-up thoughts for the first part of the course

Mar. 1: Ch 7 discussion.

Mar. 3: More Ch. 7 discussion (slides).

Mar. 8: Ch 8 (slides, with customary gaps).

Mar. 10: Ch 9 (slides).

Mar. 15 More Ch. 9 (slides). Also here is the R code I used to use Hoff's function to evaluate posterior model probabilities.

Mar. 17 Finish regression (Ch. 9), plus some `warm-up' for Ch. 10 (slides).

Mar. 22 Ch 10 (slides). Also, Metropolis-Hastings demo

Mar. 24 Finish Ch. 10, segue into partial identification (slides).

Mar. 29 Ch. 11 (slides).

Mar. 31 More Ch. 11 (slides). Also for the curious, R code for the example.

Apr. 7 Ch. 12 (slides).

Apr. 12 Hypothesis testing (sort of...) (slides).

Apr. 14 Updated top ten list!

THE END!