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Bayesian Statistics: Course Outline

The student will gain an appreciation of the importance of conditional independence in subjective (Bayesian) statistical modeling.

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Course Outline

  • Prior information.
  • Prior distribution. 
  • Posterior distributions. 
  • The posterior means.
  • Medians (Bayes estimators under loss functions).
  • Variances of univariate and bivariate posterior distributions. 
  • Non-informative priors.
  • Methods of elicitation of hyper parameters of informative priors. 
  • Bayesian Hypotheses Testing: Bayes factor.
  • The highest density region.
  • Posterior probability of the hypothesis.

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