Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics. >Written by a uniquely well-qualified team of authors >The unified framework clarifies which priors work and why >Treats computation as well as asymptotics Table of Contents Preface Glossary of symbols 1. Introduction 2. Priors on function spaces 3. Priors on spaces of probability measures 4. Dirichlet processes 5. Dirichlet process mixtures 6. Consistency: general theory 7. Consistency: examples 8. Contraction rates: general theory 9. Contraction rates: examples 10. Adaptation and model selection 11. Gaussian process priors 12. Infinite-dimensional Bernstein–von Mises theorem 13. Survival analysis 14. Discrete random structures Appendices References Author index Subject index.