Introduction Preliminaries and the Finite Dimensional Case M(X) and Priors on M(X) Dirichlet and Polya Tree Process Consistency Theorems Density Estimation Inference for Location Parameter Regression Problems Uniform Distribution on Infinite Dimensional Spaces Survivial Analysis-Dirichlet Priors Neutral To Right Priors
Introduction * Preliminaries and the Finite Dimensional Case * M(X) and Priors on M(X) * Dirichlet and Polya Tree Process * Consistency Theorems * Density Estimation * Inference for Location Parameter * Regression Problems * Uniform Distribution on Infinite Dimensional Spaces * Survivial Analysis-Dirichlet Priors * Neutral To Right Priors
This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
This book is addressed to second year graduate students in statstics and contains some results that have never appeared in a book before.