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Clustering, Cluster Inference and Applications in Clustering: Applications to the Analysis of Gene Expression Data - Couverture souple
Synopsis
Multivariate mixture models provide a convenient method of density estimation and model based clustering as well as providing possible explanations for the actual data generation process. But the problem of choosing the number of components in a statistically meaningful way is still a subject of considerable research. Available methods for estimation include, optimizing AIC and BIC, estimating the number through nonparametric maximum likelihood, hypothesis testing and Bayesian approaches with entropy distances. In our book we present several rules for selecting a finite mixture model, based on estimation and inference using a quadratic distance measure. In this book we also develop tools for determining the number of modes in a mixture of multivariate normal densities. We use these criterion to select clusters which display distinct modes. Finally we fine tune our methods to analyze gene-expression data from micro-arrays, and compare them with other competitive methods.
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- ÉditeurLAP LAMBERT Academic Publishing
- Date d'édition2011
- ISBN 10 3845423625
- ISBN 13 9783845423623
- ReliureBroché
- Langueanglais
- Nombre de pages184
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Clustering, Cluster Inference and Applications in Clustering
Edité par
LAP LAMBERT Academic Publishing, 2011
ISBN 10 : 3845423625
ISBN 13 : 9783845423623
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ray SurajitSurajit Ray is an assistant professor of Statistics in the Department of Mathematics and Statistics at Boston University. His research interests are in the area of statistical model selection, the theory and geometry of mi. N° de réf. du vendeur 5481855
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Clustering, Cluster Inference and Applications in Clustering : Applications to the Analysis of Gene Expression Data
Edité par
LAP LAMBERT Academic Publishing, 2011
ISBN 10 : 3845423625
ISBN 13 : 9783845423623
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Multivariate mixture models provide a convenient method of density estimation and model based clustering as well as providing possible explanations for the actual data generation process. But the problem of choosing the number of components in a statistically meaningful way is still a subject of considerable research. Available methods for estimation include, optimizing AIC and BIC, estimating the number through nonparametric maximum likelihood, hypothesis testing and Bayesian approaches with entropy distances. In our book we present several rules for selecting a finite mixture model, based on estimation and inference using a quadratic distance measure. In this book we also develop tools for determining the number of modes in a mixture of multivariate normal densities. We use these criterion to select clusters which display distinct modes. Finally we fine tune our methods to analyze gene-expression data from micro-arrays, and compare them with other competitive methods. N° de réf. du vendeur 9783845423623
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Clustering, Cluster Inference and Applications in Clustering
Edité par
LAP LAMBERT Academic Publishing Jul 2011, 2011
ISBN 10 : 3845423625
ISBN 13 : 9783845423623
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Multivariate mixture models provide a convenient method of density estimation and model based clustering as well as providing possible explanations for the actual data generation process. But the problem of choosing the number of components in a statistically meaningful way is still a subject of considerable research. Available methods for estimation include, optimizing AIC and BIC, estimating the number through nonparametric maximum likelihood, hypothesis testing and Bayesian approaches with entropy distances. In our book we present several rules for selecting a finite mixture model, based on estimation and inference using a quadratic distance measure. In this book we also develop tools for determining the number of modes in a mixture of multivariate normal densities. We use these criterion to select clusters which display distinct modes. Finally we fine tune our methods to analyze gene-expression data from micro-arrays, and compare them with other competitive methods. 184 pp. Englisch. N° de réf. du vendeur 9783845423623
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Clustering, Cluster Inference and Applications in Clustering: Applications to the Analysis of Gene Expression Data
Edité par
LAP LAMBERT Academic Publishing, 2011
ISBN 10 : 3845423625
ISBN 13 : 9783845423623
Ancien ou d'occasion
Paperback
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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Paperback. Etat : Like New. Like New. book. N° de réf. du vendeur ERICA79638454236256
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