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
Udgivelsesdato: MAR-APR
Udgivelsesdato: MAR-APR
Originalsprog | Engelsk |
---|---|
Tidsskrift | Genetics, Selection, Evolution |
Vol/bind | 40 |
Udgave nummer | 2 |
Sider (fra-til) | 161-176 |
Antal sider | 16 |
ISSN | 0999-193X |
DOI | |
Status | Udgivet - 2008 |