Springer Statistics

"There is much to like about the book under review. The authors present Bayesian nonparametric statistics focusing on how it is applied in data analysis. ... This is a book for a statistician or graduate student that has accepted the Bayesian approach and would like to know more about Bayesian approaches to nonparametric problems." (Ross S. McVinish, Mathematical Reviews, February, 2016)

"The book provides a rich review of Bayesian nonparametric methods and models with a wealth of illustrations ranging from simple examples to more elaborated applications on case studies considered in recent literature. ... the book succeeds in the difficult task of providing a rather complete, yet coincise, overview. Overall, the nature of the book makes it a suitable reference for both practitioners and theorists." (Bernardo Nipoti, zbMATH 1333.62003, 2016)

"Methods are illustrated with a wealth of examples, ranging from stylised applications to case studies from recent literature. The book is a good reference for statisticians interested in Bayesian non-parametric data analysis. It is well-written and structured. Readers can find the algorithms, examples and applications easy to follow and extremely useful. This book makes a good contribution to the literature in the area of Bayesian non-parametric statistics." (Diego Andres Perez Ruiz, International Statistical Review, Vol. 84 (1), 2016)

"Book provides a brief overview and introduction of the subject, points to associated theoretical and applied literature, guides the interested reader to the most important and established methods in a wealth of methods where one can easily get lost, and encourages their application. At the same time, hints to the powerful and comprehensive R package DPpackage, which comprises most of the discussed methods in a unifying, easily accessible interface, greatly reduces the barriers to the use of nonparametric Bayesian methods." (Manuel Wiesenfarth, Biometrical Journal, Vol. 58 (4), 2016)