Bayesian Inference for Gene Expression and Proteomics

Do, Kim-Anh

ISBN 10: 1107636981 / ISBN 13: 9781107636989

Edité par Cambridge University Press, 2012

A propos de cet article

Firm, square and tight, just showing a few minor rubs. Hence a non-text page is stamped 'damaged'. Despite such this book looks and feels unread and is actually in nearly new condition. Thus the contents are crisp and fresh. Now offered for sale at a special bargain price.

N° de réf. du vendeur 120895

Détails bibliographiques

• Titre: Bayesian Inference for Gene Expression and Proteomics
• Éditeur: Cambridge University Press
• Date de parution: 2012
• Langue: anglais
• ISBN à 10 chiffres: 1107636981
• ISBN à 13 chiffres: 9781107636989
• Reliure: Paperback
• Édition: First Edition.
• État de l'article: Like New
• Catalogues du vendeur:
  • Science, Mathematics & Technology

A propos de ce titre

Synopsis

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

À propos des auteurs

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.