University of Cambridge > Talks.cam > MRC Biostatistics Unit Seminars > Transdimensional sampling algorithms for Bayesian variable selection in classification problems with many more variables than observations

Transdimensional sampling algorithms for Bayesian variable selection in classification problems with many more variables than observations

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If you have a question about this talk, please contact Nikolaos Demiris.

One problem in microarray analysis is the identification of a (small) collection of genes that discriminate between two classes (such as diseased and not diseased). Variable selection in probit regression is one statistical approach to this problem. We perform a Bayesian analysis using Markov chain Monte Carlo methods with standard data augmentation techniques. The main issue is how to efficiently move between different models (combination of variables) using Reversible Jump MCMC . In this talk, I will discuss the construction and efficiency of different proposals. High between model acceptance rates are one distinctive feature of many data sets that we consider and this suggests using ideas motivated by Metropolis-Hastings random walk samplers to build proposals that have (near) optimal mixing. The methods will be illustrated on several gene expression data sets. This is joint work with Demetris Lamnisos and Mark Steel. A technical report is available at http://www.kent.ac.uk/ims/personal/jeg28/trans.pdf

This talk is part of the MRC Biostatistics Unit Seminars series.

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