Bayesian Inference: Course Outline
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available.
EBooks (Full Text)

by Lee, Peter M.
Publisher: John Wiley & Sons
ISBN: 9781118359754 
by Rossi, Peter E.
Allenby, Greg M.
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 noninformation 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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