Bayesian Statistics Using Conjugate Priors

In this chapter we go over the cases where the posterior distribution can be found easily, without having to do any numerical integration. In these cases, the observations come from a distribution that is a member of the exponential family of distributions, and a conjugate prior is used. The methods developed in this chapter will be used in later chapters as steps to help us draw samples from the posterior for more complicated models having many parameters. 

---

Chapter 4 reviews Bayesian statistics using conjugate priors. These are the classical models that have analytic solutions to the posterior. In computational Bayesian statics, these are useful tools for drawing an individual parameter as steps of the Markov chain Monte Carlo algorithms. 

---