10.12 Summary

In this chapter, we have taken a conceptual walk through some of the most common methods used to compute the posterior distribution. We have put special emphasis on MCMC methods, which are designed to work on any given model (or at least a broad range of models), and thus are sometimes called universal inference engines. These methods are the core of any probabilistic programming language as they allow for automatic inference, letting users concentrate on iterative model design and interpretations of the results.

We also discussed numerical and visual tests for diagnosing samples. Without good approximations of the posterior distribution, all the advantages and flexibility of the Bayesian framework vanish. Thus, evaluating the quality of the samples is a crucial step before doing any other type of analysis.