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- Exponential Families, Likelihoods, Prior and Posterior Distributions, Conjugate Priors, Models for Normal Data, Multivariate Normal, Shrinkage, Bayesian Linear Models, Informative and Noninformative Priors, Monte Carlo Integration, Rejection and Importance Sampling.
- Markov Chains, The Gibbs Sampler, Hierarchical Models, Exchangeability, Linear Models Revisited, MCMC Algorithms, Empirical Bayes, Sensitivity Analysis, Hypothesis Testing, Bayes Factor, Model Choice vs. Model Averaging, Stochastic Variable Selection, Kalman Filter.
- Sequential Monte Carlo, Bayesian statistics and R, Single and Multi-Parameter Models, Gibbs Sampling, Metropolis-Hastings Sampling, Sampling: Problems, Techniques, and Postestimation, Linear Regression Models, Generalized Linear Models.
- Hierarchical Models, Multivariate Models, Bayesian Model of Inference, On Priors and Estimates, Hypothesis Testing and Robustness, Computation, Monte Carlo Methods and Markov Chains, Hierarchical Linear Model, Discrete Choice Models, Models for Event Counts and Durations, Measurement Models
- Bayesian methodology with emphasis on applied statistical problem, data displaying, prior distribution elicitation, posterior analysis, models for proportions, means and regression.
- Empirical Bayes procedures for estimation
- Testing and prediction.