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

• Main Authors: Sorensen Daniel, Ibánẽz-Escriche Noelia, Waagepetersen Rasmus
• Format: Article
• Language: deu
• Published: BMC 2008-03-01
• Series: Genetics Selection Evolution
• Subjects: Langevin-Hastings; Markov chain Monte Carlo; normal approximation; proposal distributions; reparameterization
• Online Access: http://www.gsejournal.org/content/40/2/161

<p>Abstract</p> <p>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.</p>