Bayesian Nonparametric Data Analysis

Peter Müller

ISBN 10: 3319189670 / ISBN 13: 9783319189673

Edité par Springer International Publishing, Springer Nature Switzerland, 2015

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9783319189673

Détails bibliographiques

• Titre: Bayesian Nonparametric Data Analysis
• Éditeur: Springer International Publishing, Springer Nature Switzerland
• Date de parution: 2015
• Langue: anglais
• ISBN à 10 chiffres: 3319189670
• ISBN à 13 chiffres: 9783319189673
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 241 L x 160 H x 17 P (en millimètres) 241 millimètres de largeur par 160 millimètres de hauteur par 17 millimètres de profondeur
• Poids: 483 grammes

A propos de ce titre

Synopsis

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.

À propos de l?auteur

Peter Mueller is Professor in the Department of Mathematics and the Department of Statistics & Data Science at the University of Texas at Austin. He has published widely on nonparametric Bayesian statistics, with an emphasis on applications in biostatistics and bioinformatics.

Fernando Andrés Quintana is Professor in the Department of Statistics at Pontificia Universidad Catolica de Chile with interests in nonparametric Bayesian analysis and statistical computing. His publications include extensive work on clustering methods and applications in biostatistics.

Alejandro Jara is Associate Professor in the Department of Statistics at Pontificia Universidad Catolica de Chile, with research interests in nonparametric Bayesian statistics, Markov chain Monte Carlo methods and statistical computing. He developed the R package "DPpackage," a widely used public domain set of programs for inference under nonparametric Bayesian models.

Timothy Hanson is Professor of Statistics in the Department of Statistics at the University of South Carolina. His research interests include survival analysis, nonparametric regression