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OverviewThis 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.Contents: IntroductionMathematical Description of Nonlinear Mixture ModelsLabel Switching and TrappingTreatment of Mixture Models with an Unknown Number of ComponentsApplications of BDMCMC, KLMCMC, and RPSNonparametric MethodsBayesian Clustering MethodsReadership: Graduate students and researchers in bioinformatics, mathematical biology, probability and statistics, mathematical modeling, and pharmacokinetics.Key Features: Looks at the issue of inequality in Singapore in a multi faceted mannerRaises key concerns such as stagnating wage levels, barriers to upward mobility, healthcare affordability and income vulnerability in retirementWritten in a free flowing style that is suitable for general readership Full Product DetailsAuthor: Tatiana Tatarinova , Alan SchumitzkyPublisher: Imperial College Press Imprint: Imperial College Press ISBN: 9781322669113ISBN 10: 1322669112 Pages: 296 Publication Date: 01 January 2014 Audience: General/trade , General Format: Electronic book text Publisher's Status: Active Availability: Available To Order We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately. Table of ContentsReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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