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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.

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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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