**Course Objectives:** | This course introduces the basic theoretical and applied
principles of Bayesian statistical analysis in a manner geared
toward students in the social sciences. The Bayesian paradigm
is particularly useful for the type of data that social
scientists encounter given its recognition of the mobility of
population parameters, its ability to incorporate information
from prior research, and its ability to update estimates as new
data are observed. The course will begin with a discussion of
the strengths of the Bayesian approach for social science data
and the philosophical differences between Bayesian and
frequentist analyses. Next, the course will cover the
theoretical underpinnings of Bayesian modeling and provide a
brief introduction to the primary estimation algorithms. The
bulk of the course will focus on estimating and interpreting
Bayesian models from an applied perspective. Students will be
introduced to the Bayesian forms of the standard statistical
models taught in regression and MLE courses (i.e., normal,
logit/probit, Poisson, etc.). This course assumes a solid
understanding of the linear model and matrix algebra and some
exposure to models with limited dependent variables. The course
will rely heavily on R and WinBUGS for estimation. Prior
experience with these software packages is preferred but not
assumed. Note: Although this course will cover some of the
basics of MCMC and the Gibbs Sampler (among other sampling
algorithms), application/interpretation will be the primary
focus. For this reason, students already familiar with the
basics of Bayesian modeling using WinBUGS, MCMC-pack, JAGS or
some other software may find the Bayesian course offered in the
second session more appropriate. |