This book provides a class of highly flexible models that can accurately describe and predict many types of real data sets. There are a large number of applications from a wide range of scientific fields including economics, medicine, bioinformatics, genetics, psychology, education, sociology, and engineering. The book provides an accessible and self-contained introduction to the ideas underpinning Bayesian nonparametrics with lots of real data examples, computing using R, MATLAB and WinBUGS, and exercises for course use or self-study.