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 20 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 com-
binations. 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, mak-
ing the book valuable for graduate students and researchers alike in statistics and
machine learning, as well as application areas such as econometrics and biostatistics.