Bayesian Nonparametric Data Analysis

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Éditeur :

Springer


Collection :

Springer Series in Statistics

Paru le : 2015-06-17

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Description
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.
Pages
193 pages
Collection
Springer Series in Statistics
Parution
2015-06-17
Marque
Springer
EAN papier
9783319189673
EAN PDF
9783319189680

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
19
Taille du fichier
5069 Ko
Prix
116,04 €
EAN EPUB
9783319189680

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
19
Taille du fichier
3328 Ko
Prix
116,04 €