Three Different Gibbs Samplers for BayesB Genomic Prediction
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2014-01-01
Authors
Cheng, Hao
Fernando, Rohan
Garrick, Dorian
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Altmetrics
Abstract
Typical implementations of genomic prediction utilize Markov chain Monte Carlo (MCMC) sampling to estimate effects. Metropolis-Hastings (MH) is a commonly-used algorithm. We considered three different Gibbs samplers to speed up BayesB, a commonly-used model for genomic prediction. These differ in the manner they sample the marker effect, the locus-specific variance and the indicator variable. They are a single-site Gibbs Sampler, a blocking Gibbs Sampler and a Gibbs Sampler with pseudo prior. These three versions of BayesB are about twice as fast as the one using a MH algorithm.
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Animal Science Research Reports
ASL R2867
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report
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Wed Jan 01 00:00:00 UTC 2014