This book reviews nonparametric Bayesian methods and models that have
proven useful in the context of data analysis. Rather than providing an
encyclopedic review of probability models, the book's structure follows
a data analysis perspective. As such, the chapters are organized by
traditional data analysis problems. In selecting specific nonparametric
models, simpler and more traditional models are favored over specialized
ones.
The discussed methods are illustrated with a wealth of examples,
including applications ranging from stylized examples to case studies
from recent literature. The book also includes an extensive discussion
of computational methods and details on their implementation. R code for
many examples is included in online software pages.