Bayesian Inference for Gene Expression and Proteomics - Brossura

Brossura

ISBN 10: 1107636981 ISBN 13: 9781107636989

Casa editrice: Cambridge University Press, 2012

Riassunto

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

Su questo libro

Informazioni sugli autori

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.

Dettagli dell'edizione

• Editore: Cambridge University Press
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 1107636981
• ISBN 13: 9781107636989
• Rilegatura: Copertina flessibile
• Numero di pagine: 468
• Redattore: Do Kim-Anh
• Contatto del produttore: non disponibile
• Persona responsabile: Avery; https://www.avery.fr/; ; chemin des peupliers 27; Dardilly; Francia