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Genet Sel Evol. 2008 Mar-Apr;40(2):161-76. doi: 10.1051/gse:2007042. Epub 2008 Feb 27.

A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics.

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  • 1Department of Mathematical Sciences, Aalborg University, 9220 Aalborg, Denmark. rw@math.aau.dk


In quantitative genetics, Markov chain Monte Carlo (MCMC) methods are indispensable for statistical inference in non-standard models like generalized linear models with genetic random effects or models with genetically structured variance heterogeneity. A particular challenge for MCMC applications in quantitative genetics is to obtain efficient updates of the high-dimensional vectors of genetic random effects and the associated covariance parameters. We discuss various strategies to approach this problem including reparameterization, Langevin-Hastings updates, and updates based on normal approximations. The methods are compared in applications to Bayesian inference for three data sets using a model with genetically structured variance heterogeneity.

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