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.