Fast genomic prediction of breeding values using parallel Markov chain Monte Carlo with convergence diagnosis.

ABSTRACT:

Background

Running multiple-chain Markov Chain Monte Carlo (MCMC) provides an efficient parallel computing method for complex Bayesian models, although the efficiency of the approach critically depends on the length of the non-parallelizable burn-in period, for which all simulated data are discarded. In practice, this burn-in period is set arbitrarily and often leads to the performance of far more iterations than required. In addition, the accuracy of genomic predictions does not improve after the MCMC reaches equilibrium.

Results

Automatic tuning of the burn-in length for running multiple-chain MCMC was proposed in the context of genomic predictions using BayesA and BayesCπ models. The performance of parallel computing versus sequential computing and tunable burn-in MCMC ve

SUBMITTER: Guo P 

PROVIDER: S-EPMC5751823 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature