| Lehrinhalte |
(*)Basic concepts: Bayes' theorem, prior distribution, posterior distribution
conjugate analysis
Bayesian inference: point and interval estimation, hypothesis testing, model choice (marginal likelihood, Bayes factor), Bayesian prediction, posterior predictive model checks, asymptotics
priors: natural conjugate priors in exponential families, improper priors, Jeffrey's prior
introduction to MCMC methods: Metropolis Hastings algorithm, Gibbs sampling, data augmentation
Bayes analysis of statistical models: linear regression models logit and ordinal logit model; finite mixture model, random effects models
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