A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics
<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 particu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | deu |
| Published: |
BMC
2008-03-01
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| Series: | Genetics Selection Evolution |
| Subjects: | |
| Online Access: | http://www.gsejournal.org/content/40/2/161 |
| Summary: | <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> |
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| ISSN: | 0999-193X 1297-9686 |