Evolutionary Stochastic Search for Bayesian model exploration

Statistics – Computation

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

Implementing Bayesian variable selection for linear Gaussian regression models for analysing high dimensional data sets is of current interest in many fields. In order to make such analysis operational, we propose a new sampling algorithm based upon Evolutionary Monte Carlo and designed to work under the "large p, small n" paradigm, thus making fully Bayesian multivariate analysis feasible, for example, in genetics/genomics experiments. Two real data examples in genomics are presented, demonstrating the performance of the algorithm in a space of up to 10,000 covariates. Finally the methodology is compared with a recently proposed search algorithms in an extensive simulation study.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Evolutionary Stochastic Search for Bayesian model exploration does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Evolutionary Stochastic Search for Bayesian model exploration, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Evolutionary Stochastic Search for Bayesian model exploration will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-324064

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.