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

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...

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Bibliographic Details
Main Authors: Peng Guo, Bo Zhu, Hong Niu, Zezhao Wang, Yonghu Liang, Yan Chen, Lupei Zhang, Hemin Ni, Yong Guo, El Hamidi A. Hay, Xue Gao, Huijiang Gao, Xiaolin Wu, Lingyang Xu, Junya Li
Format: Article
Language:English
Published: BMC 2018-01-01
Series:BMC Bioinformatics
Subjects:
Bayesian models
Convergence diagnosis
Genomic prediction
High-performance computing
Tunable burn-in
Online Access:http://link.springer.com/article/10.1186/s12859-017-2003-3

Internet

http://link.springer.com/article/10.1186/s12859-017-2003-3

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