- Some useful technical results that are used to obtain the posterior distributions and posterior predictive distributions in this appendix (Section H.1).
- A formal presentation of Bayes’ theorem (Section H.2).
- The definition of conjugate prior distributions with examples for the binomial, Poisson, and normal distributions (Section H.3).
- The definition of Jeffreys prior distributions with examples for the binomial, Poisson, and normal distributions. Also a modified Jeffreys prior distribution for the normal distribution (Section H.4).
- The definition of posterior predictive distributions with examples, using conjugate prior distributions, for the binomial, Poisson, and normal distributions (Section H.5).
- Posterior predictive distributions for the binomial and the Poisson distributions using Jeffreys prior distributions. Also a posterior predictive distribution for the normal distributions using a modified Jeffreys prior distribution (Section H.6).
H.1 Basic Results Used in This Appendix
In this appendix we use the following results:
We frequently need to compute the marginal distribution corresponding to a mixture of two normal distributions. The following result is useful in making those computations.
Consider the continuous normal-normal ...