Аннотация:Abstract The problem of estimating many normal means is approached by means of an hierarchical model. The hierarchical model is the standard conjugate model with one exception: the normal distribution at the middle stage is replaced by a Dirichlet process with a normal shape. Estimation for this model is accomplished through the implementation of the Gibbs sampler (see Escobar and West,1991)Thisarticle describes a new Gibbs sampler algorithm that is implemented on a collapsed state space Results that apply to a general setting are obtained, suggesting that a collapse of the state space willimprove the rate of convergence of the Gibbs sampler. An example shows that the proposed collapse of the state space may result in a dramatically improved algorithm Keywords: Gibbs samplerhierarchical modelMarkov chainnonparametric Bayes
