# Bayesian Statistics: Course Outline

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

## E-Books (Full Text)

## 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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