Articoli correlati a Bayesian Nonparametric Data Analysis
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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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull?autore
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
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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 on-line software pages.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Bayesian Nonparametric Data Analysis
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Springer, Heidelberg, New York, Dordrecht, 2015
ISBN 10: 3319189670
ISBN 13: 9783319189673
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Hardcover. Octavo; VG-; brown/orange spine with white and brown text; no jacket; cloth exterior shows slight wear; minor rubbing to corners; text block exterior edges have light wear; tight binding; small darkish smudge to rear head edge; interior clean; illustrated; pp 193. 1344774. FP New Rockville Stock. Codice articolo 1344774
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Bayesian Nonparametric Data Analysis (Springer Series in Statistics)
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Springer-Verlag Publishing, 2015
ISBN 10: 3319189670
ISBN 13: 9783319189673
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Condizione: Like New. Fine/As New; Hardcover; Covers are still glossy with "sharp" edge-corners; Unblemished textblock edges; The endpapers and text pages are all bright and unmarked; The binding is tight with a straight spine; This book will be shipped in a sturdy cardboard box with foam padding; Medium Format (8.5" - 9.75" tall); 0.9 lbs; Tan covers with title in white lettering; 2015, Springer-Verlag Publishing; 207 pages; "Bayesian Nonparametric Data Analysis (Springer Series in Statistics)," by Peter Muller, et al. Codice articolo SKU-1032AA01503314
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Bayesian Nonparametric Data Analysis
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Springer International Publishing, 2015
ISBN 10: 3319189670
ISBN 13: 9783319189673
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Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This is the first text to introduce nonparametric Bayesian inference from a data analysis perspectiveIncludes a large number of examples to illustrate the application of nonparametric Bayesian models for important statistical inference Problems. Codice articolo 35204653
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Bayesian Nonparametric Data Analysis
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Bayesian Nonparametric Data Analysis (Springer Series in Statistics)
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Bayesian Nonparametric Data Analysis
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 208 pp. Englisch. Codice articolo 9783319189673
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Buch. Condizione: Neu. 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. Codice articolo 9783319189673
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Buch. Condizione: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 208 pp. Englisch. Codice articolo 9783319189673
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