Bayesian Nonparametric Data Analysis (Hardcover, 2015 ed.)

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:	June 2015
First published:	2015
Authors:	Peter Muller • Fernando Andres Quintana • Alejandro Jara • Tim Hanson
Dimensions:	235 x 155 x 17mm (L x W x T)
Format:	Hardcover - Cloth over boards
Pages:	193
Edition:	2015 ed.
ISBN-13:	978-3-319-18967-3
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software
LSN:	3-319-18967-0
Barcode:	9783319189673

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