ABSTRACT
The early chapters of the book describe simulation approaches that work in low-dimensional problems. With complicated models, it is rare that samples from the posterior distribution can be obtained directly, and Chapters 11 and 12 describe iterative simulation algorithms that can be used with such models. In this chapter we describe various approaches based on distributional approximations. These methods are useful for quick inferences, as starting points for Markov chain simulation algorithms, and for large problems where iterative simulation approaches are too slow. The approximations that we describe are relatively simple to compute and can provide valuable information about the fit of the model.
