Bayesian Nonparametric Mixture Models Methods and Applications Chapman & Hall/CRC Texts in Statistical Science Series

Bayesian nonparametric methods, in particular nonparametric mixture models, have become extremely popular in the last 10 years and are becoming part of the foundational material that any Bayesian statistician needs to be familiar with. This book introduces Bayesian nonparametric mixture models to readers with intermediate knowledge of Bayesian statistical methods and computation using simulation-based methods such as Markov chain Monte Carlo. Suitable for professional statisticians and graduate students, it is one of the first books to offer an introductory text on Bayesian nonparametric mixture modeling.

Introduction. Dirichlet Process Mixture Models: Theory and Methods. Dirichlet Process Mixture Models: Posterior Inference. Nonparametric Mixture Methodology for Random Effects Distributions and Generalized Linear Models. Bayesian Nonparametric Mixture Modeling for Regression Settings. Dependent Nonparametric Mixture Models. Spatial and Spatio-Temporal Modeling with Dependent Nonparametric Priors. More General Nonparametric Mixture Models. Conclusions/Discussion.