Nonlinear mixture models: a Bayesian approach
This book, written by two mathematicians from the University of Southern California, provides a broad introduction to the important subject of nonlinear mixture models from a Bayesian perspective. It contains background material, a brief description of Markov chain theory, as well as novel algorithm...
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
London
Imperial College Press
c2015
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Online-Zugang: | FHN01 Volltext |
Zusammenfassung: | This book, written by two mathematicians from the University of Southern California, provides a broad introduction to the important subject of nonlinear mixture models from a Bayesian perspective. It contains background material, a brief description of Markov chain theory, as well as novel algorithms and their applications. It is self-contained and unified in presentation, which makes it ideal for use as an advanced textbook by graduate students and as a reference for independent researchers. The explanations in the book are detailed enough to capture the interest of the curious reader, and complete enough to provide the necessary background material needed to go further into the subject and explore the research literature. In this book the authors present Bayesian methods of analysis for nonlinear, hierarchical mixture models, with a finite, but possibly unknown, number of components. These methods are then applied to various problems including population pharmacokinetics and gene expression analysis. In population pharmacokinetics, the nonlinear mixture model, based on previous clinical data, becomes the prior distribution for individual therapy. For gene expression data, one application included in the book is to determine which genes should be associated with the same component of the mixture (also known as a clustering problem). The book also contains examples of computer programs written in BUGS. This is the first book of its kind to cover many of the topics in this field |
Beschreibung: | xxv, 269 p. ill. (some col.) |
ISBN: | 9781848167575 |
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Datensatz im Suchindex
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author | Tatarinova, Tatiana V. |
author_facet | Tatarinova, Tatiana V. |
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dewey-search | 519.2/33 |
dewey-sort | 3519.2 233 |
dewey-tens | 510 - Mathematics |
discipline | Mathematik Wirtschaftswissenschaften |
format | Electronic eBook |
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id | DE-604.BV044633501 |
illustrated | Illustrated |
indexdate | 2024-07-10T07:57:42Z |
institution | BVB |
isbn | 9781848167575 |
language | English |
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physical | xxv, 269 p. ill. (some col.) |
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publishDate | 2015 |
publishDateSearch | 2015 |
publishDateSort | 2015 |
publisher | Imperial College Press |
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spelling | Tatarinova, Tatiana V. Verfasser aut Nonlinear mixture models a Bayesian approach Tatiana Tatarinova, Alan Schumitzky London Imperial College Press c2015 xxv, 269 p. ill. (some col.) txt rdacontent c rdamedia cr rdacarrier This book, written by two mathematicians from the University of Southern California, provides a broad introduction to the important subject of nonlinear mixture models from a Bayesian perspective. It contains background material, a brief description of Markov chain theory, as well as novel algorithms and their applications. It is self-contained and unified in presentation, which makes it ideal for use as an advanced textbook by graduate students and as a reference for independent researchers. The explanations in the book are detailed enough to capture the interest of the curious reader, and complete enough to provide the necessary background material needed to go further into the subject and explore the research literature. In this book the authors present Bayesian methods of analysis for nonlinear, hierarchical mixture models, with a finite, but possibly unknown, number of components. These methods are then applied to various problems including population pharmacokinetics and gene expression analysis. In population pharmacokinetics, the nonlinear mixture model, based on previous clinical data, becomes the prior distribution for individual therapy. For gene expression data, one application included in the book is to determine which genes should be associated with the same component of the mixture (also known as a clustering problem). The book also contains examples of computer programs written in BUGS. This is the first book of its kind to cover many of the topics in this field Markov processes Bayesian statistical decision theory Nonparametric statistics Multivariate analysis Bayes-Regel (DE-588)4144221-0 gnd rswk-swf Markov-Kette (DE-588)4037612-6 gnd rswk-swf Bayes-Regel (DE-588)4144221-0 s Markov-Kette (DE-588)4037612-6 s 1\p DE-604 Schumitzky, Alan Sonstige oth Erscheint auch als Druck-Ausgabe 9781848167568 http://www.worldscientific.com/worldscibooks/10.1142/P794#t=toc Verlag URL des Erstveroeffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
spellingShingle | Tatarinova, Tatiana V. Nonlinear mixture models a Bayesian approach Markov processes Bayesian statistical decision theory Nonparametric statistics Multivariate analysis Bayes-Regel (DE-588)4144221-0 gnd Markov-Kette (DE-588)4037612-6 gnd |
subject_GND | (DE-588)4144221-0 (DE-588)4037612-6 |
title | Nonlinear mixture models a Bayesian approach |
title_auth | Nonlinear mixture models a Bayesian approach |
title_exact_search | Nonlinear mixture models a Bayesian approach |
title_full | Nonlinear mixture models a Bayesian approach Tatiana Tatarinova, Alan Schumitzky |
title_fullStr | Nonlinear mixture models a Bayesian approach Tatiana Tatarinova, Alan Schumitzky |
title_full_unstemmed | Nonlinear mixture models a Bayesian approach Tatiana Tatarinova, Alan Schumitzky |
title_short | Nonlinear mixture models |
title_sort | nonlinear mixture models a bayesian approach |
title_sub | a Bayesian approach |
topic | Markov processes Bayesian statistical decision theory Nonparametric statistics Multivariate analysis Bayes-Regel (DE-588)4144221-0 gnd Markov-Kette (DE-588)4037612-6 gnd |
topic_facet | Markov processes Bayesian statistical decision theory Nonparametric statistics Multivariate analysis Bayes-Regel Markov-Kette |
url | http://www.worldscientific.com/worldscibooks/10.1142/P794#t=toc |
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