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.
This site is maintained by: draper@ucsc.edu
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