Analysis of Biological Time Series Data in Data-Rich Situations

Tatiana Tatarinova

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddCyfraniad i gynhadleddadolygiad gan gymheiriaid

Crynodeb

We presented a Bayesian analysis of nonlinear hierarchical mixture models with a finite but unknown number of components. Our approach is based on Markov chain Monte Carlo (MCMC) methods. One of the applications of our method is directed to the clustering problem in gene expression analysis. From a mathematical and statistical point of view, we discuss the following topics: theoretical and practical convergence problems of the MCMC method; determination of the number of components in the mixture; and computational problems associated with likelihood calculations. In the existing literature, these problems have mainly been addressed in the linear case. One of the main contributions of this paper is developing a method for the nonlinear case. Our approach is based on a combination of methods including Gibbs sampling, random permutation sampling, birth-death MCMC, and Kullback-Leibler distance.
Iaith wreiddiolSaesneg
TeitlN/A
StatwsCyhoeddwyd - 15 Gorff 2010
Digwyddiad 2010 Summer Program on Semiparametric Bayesian Inference: Applications in Pharmacokinetics and Pharmacodynamics - Location unknown - please update
Hyd: 15 Jul 201015 Jul 2010

!Presentation

!Presentation 2010 Summer Program on Semiparametric Bayesian Inference: Applications in Pharmacokinetics and Pharmacodynamics
Cyfnod15/07/1015/07/10

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