To understand what the Bayesian approach to statistical modeling is and to appreciate the differences between the Bayesian and Frequentist approaches. The students will be able to estimate a wide variety of models in the Bayesian framework and to adjust example code to fit their specific modeling needs.
-Theory/foundations of the Bayesian approach including:
-objective vs subjective probability
-how to derive and incorporate prior information
-the basics of MCMC sampling
-assessing convergence of Markov Chains -Bayesian difference of means/ANOVA
-Bayesian versions of: Linear models, logit/probit (dichotomous/ordered/unordered choice models), Count models, Latent variable and measurement models, Multilevel models
-presentation of results