Bayesian Inference: Course Outline

• Prior information and Prior distribution;
• Posterior distribution;
• Summaries of the univariate, bivariate & multivariate posterior distributions & applications.
• Posterior distributions using conjugate prior.
• Elicitation of hyper parameters of information priors.
• Methods for derivation of non-information priors (reference priors).
• Predictive distribution;
• Predictive inference. Bayesian hypothesis testing;
• Bayes factor for testing the sharp (point) hypothesis;
• The highest density region.
• Bayesian computation, e.g. Gibbs sampling.

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