This is the web page for STAT 206 (winter 2021). In what follows DD = David Draper (professor; email address draper@ucsc.edu) and IG  = Isabelle Grenier  (TA; email address igrenier@ucsc.edu).

The catalog description for STAT 206 is as follows:

Introduces Bayesian statistical modeling from a practitioner's perspective. Covers basic concepts (e.g., prior-posterior updating, Bayes factors, conjugacy, hierarchical modeling, shrinkage, ...), computational tools (Markov chain Monte Carlo, Laplace approximations), and Bayesian inference for some specific models widely used in the literature (linear and generalized linear mixed models). Prerequisite(s): course 131 or 203, or by permission of the instructor. Enrollment is restricted to graduate students except by instructor permission.

• (4 Jan 2021) Announcements will be posted in this section. The first Attachment section below will contain (scanned) PDF copies of the lecture notes, document camera notes and extra notes, as well as case studies and R and WolframAlpha code; the second Attachment section will contain secure documents and is invisible until you log into the web page with your CruzID Blue password (click on the tiny 'Log In' text in the lower right corner).
• (5 Jan 2021) When you email IG or me, please put STAT 206 winter 2021 in the subject line; this will enable us to get back to you as quickly as possible.