Bayes, Bootstrap, and Moments

This chapter presents a Bayesian analysis of semiparametric models in which the vector of parameters of interest is characterized by a moment equation. Powerful representations of the Dirichlet processes are used and provide an efficient numerical strategy to deal with such models. The so-called noninformative prior specification gives a Bayesian interpretation of the bootstrap method, but some pathologies of this prior measure are pointed out. Several numerical applications illustrate the presentation.