A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics |
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Authors: | Rasmus Waagepetersen Noelia Ibán?z-Escriche Daniel Sorensen |
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Affiliation: | 1.Department of Mathematical Sciences, Aalborg University, 9220 Aalborg, Denmark;2.IRTA, Avda. Rovira Roure, 25198 Lleida, Spain;3.Department of Genetics and Biotechnology, Danish Institute of Agricultural Sciences, P.O. Box 50, 8830 Tjele, Denmark |
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Abstract: | 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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Keywords: | Langevin-Hastings Markov chain Monte Carlo normal approximation proposal distributions reparameterization |
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