Bayesian Nonparametrics

Bayesian Nonparametrics (Cambridge Series in Statistical and Probabilistic Mathematics)
Bayesian nonparametrics works – theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.

Table of Contents

1 Bayesian nonparametric methods: motivation and ideas
2 The Dirichlet process, related priors and posterior asymptotics
3 Models beyond the Dirichlet process
4 Further models and applications
5 Hierarchical Bayesian nonparametric models with applications
6 Computational issues arising in Bayesian nonparametric hierarchical models
7 Nonparametric Bayes applications to biostatistics
8 More nonparametric Bayesian models for biostatistics