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.

-- Why Bayesian statistics is appropriate for the social 
sciences. 
-- Historical development. 
-- Why are we uncertain about probability? 
-- Bayes’ law and conditional probability. 
-- Likelihood theory and estimation review. 
-- Probability review. 
-- The generalized linear model and the link function. 
-- The Bayesian setup.
-- What is a prior? 
-- Combining priors and likelihoods. 
-- Interpreting a posterior. 
-- Sampling from univariate posteriors using R.
-- The Bayesian normal model.
A LOT more on priors... 
-- Conjugacy.
-- Noninformative v. informative priors.
-- Uniform priors. 
-- Elicited priors. 
-- Assessing model quality and convergence.
-– Using R. 
-– Using WinBUGS.
-– Other methods.
-- Interpreting and presenting Bayesian model results. 
-- Binary logit/probit.
-- Ordered logit/multinomial logit. 
-- Poisson and other event count models. 
-- Bayesian forecasting from logit models. 
-- Item response theory (IRT) and ideal point estimation.
-- Other latent variable/measurement/structural models. 
–- Models with missing data.

Every student must agree to abide by UGA's academic honesty 
policy and procedures known as "A Culture of Honesty," when
applying for admission to the University of Georgia.  "A Culture
of Honesty" and the University of Georgia Student Honor Code 
work together to define a climate of academic honesty and 
integrity at the University.