Bayesian Inference for Gene Expression and Proteomics - Tapa blanda

 
9781107636989: Bayesian Inference for Gene Expression and Proteomics
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ISBN 10: 1107636981 ISBN 13: 9781107636989
Editorial: Cambridge University Press, 2012

Sinopsis

Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.

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Acerca de los autores

Kim-Anh Do is a Professor in the Department of Biostatistics and Applied Mathematics at the University of Texas M. D. Anderson Cancer Center. Her research interests are in computer-intensive statistical methods with recent focus in the development of methodology and software to analyze data produced from high-throughput optimization.

Peter Müller is a Professor in the Department of Biostatistics and Applied Mathematics at the University of Texas M. D. Anderson Cancer Center. His research interests and contributions are in the areas of Markov chain Monte Carlo posterior simulation, nonparametric Bayesian inference, hierarchical models, mixture models and Bayesian decisions problems.

Marina Vannucci is a Professor of Statistics at Rice University. Her research focuses on the theory and practice of Bayesian variable selection techniques and on the development of wavelet-based statistical models and their applications. Her work is often motivated by real problems that need to be addressed with suitable statistical methods.

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Editorial
Cambridge University Press
Año de publicación
2012
Idioma
Inglés
ISBN 10 
1107636981
ISBN 13 
9781107636989
Encuadernación
Tapa blanda
Número de páginas
468
Editor
Do Kim-Anh
Contacto del fabricante
no disponible
Persona responsable
no disponible

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9780521860925: Bayesian Inference for Gene Expression and Proteomics Hardback

Edición Destacada

ISBN 10:  052186092X ISBN 13:  9780521860925
Editorial: Cambridge University Press, 2006
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