Main topics:

(1) Bayesian hierarchical models, prior and posterior distributions, hyper-priors, posterior predictive distribution, shrinkage.
(2) Missing data problems, the EM algorithm, data augmentation, incomplete multivariate normal data.
(3) Mixture modeling, clustering algorithms, Dirichlet process.
(4) Hidden Markov models, dynamic programming, forward summation and backward sampling, Kalman filter.
(5) Bayesian networks, graphical model, DAGs, belief propagation, structural inference, model selection and model averaging.