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Nonlinear mixture models : a bayesian approach

Author: Tatiana V Tatarinova; Alan Schumitzky
Publisher: Hackensack, NJ : Imperial College Press, [2015] ©2015
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
This 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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Tatarinova, Tatiana V.
Nonlinear mixture models.
London : Imperial College Press, [2015]
(DLC) 2014038898
(OCoLC)701806866
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Tatiana V Tatarinova; Alan Schumitzky
ISBN: 9781848167575 1848167571
OCLC Number: 900633207
Description: 1 online resource (xxiv, 269 pages) : illustrations
Contents: 1. Introduction. 1.1. Bayesian approach. 1.2. Review of applications of mixture models in population pharmacokinetics. 1.3. Review of applications of mixture models to problems in computational biology. 1.4. Outline of the book --
2. Mathematical description of nonlinear mixture models. 2.1. Fundamental notions of Markov chain Monte Carlo. 2.2. Nonlinear hierarchical models. 2.3. Gibbs sampling. 2.4. Prior distributions: Linear and nonlinear cases --
3. Label switching and trapping. 3.1. Label switching and permutation invariance. 3.2. Markov chain convergence. 3.3. Random permutation sampler. 3.4. Re-parametrization. 3.5. Stephens' approach: Relabeling strategies 4. Treatment of mixture models with an unknown number of components. 4.1. Introduction. 4.2. Finding the optimal number of components using weighted Kullback-Leibler distance. 4.3. Stephens' approach: Birth-death Markov chain Monte Carlo. 4.4. Kullback-Leibler Markov chain Monte Carlo --
A new algorithmfor finite mixture analysis --
5. Applications of BDMCMC, KLMCMC, and RPS. 5.1. Galaxy data. 5.2. Simulated nonlinear normal mixture model. 5.3. Linear normal mixture model: Boys and girls. 5.4. Nonlinear pharmacokinetics model and selection of prior distributions. 5.5. Nonlinear mixture models in gene expression studies 6. Nonparametric methods. 6.1. Definition of the basic model. 6.2. Nonparametric maximum likelihood. 6.3. Nonparametric Bayesian approach. 6.4. Gibbs sampler for the Dirichlet process. 6.5. Nonparametric Bayesian examples. 6.6. Technical notes. 6.7. Stick-breaking priors. 6.8. Examples of stick-breaking. 6.9. Maximum likelihood and stick-breaking (A connection between NPML and NPB approaches) --
7. Bayesian clustering methods. 7.1. Brief review of clustering methods in microarray analysis. 7.2. Application of KLMCMC to gene expression time-series analysis. 7.3. Kullback-Leibler clustering. 7.4. Simulated time-series data with an unknown number of components (Zhou model). 7.5. Transcription start sites prediction. 7.6. Conclusions.
Responsibility: Tatiana Tatarinova, University of Glamorgan, UK, Alan Schumitzky, University of Southern California, USA.

Abstract:

Provides an introduction to the important subject of nonlinear mixture models from a Bayesian perspective. This title contains background material, a brief description of Markov chain theory, as well  Read more...

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