Bayesian Nonparametric Data Analysis (Paperback, Softcover reprint of the original 1st ed. 2015)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Springer Series in Statistics
Release date:	October 2016
First published:	2015
Authors:	Peter Müller • Fernando Andres Quintana • Alejandro Jara • Tim Hanson
Dimensions:	235 x 155 x 11mm (L x W x T)
Format:	Paperback
Pages:	193
Edition:	Softcover reprint of the original 1st ed. 2015
ISBN-13:	978-3-319-36842-9
Categories:	Books > Computing & IT > General Books > Science & Mathematics > Mathematics > Probability & statistics
LSN:	3-319-36842-7
Barcode:	9783319368429

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