Data from: Bayesian adaptive Markov Chain Monte Carlo estimation of genetic parameters

Boby Mathew, Andrea M. Bauer, Petri Koistinen, Tobias C. Reetz, Jens Léon & Mikko J. Sillanpää
Accurate and fast estimation of genetic parameters that underlie quantitative traits using mixed linear models with additive and dominance effects is of great importance in both natural and breeding populations. Here we propose a new fast adaptive Markov Chain Monte Carlo (MCMC) sampling algorithm for the estimation of genetic parameters in the linear mixed model with several random effects. In the learning phase of our algorithm, we use the hybrid Gibbs sampler to learn the...
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