tailieunhanh - Báo cáo sinh học: " Bayesian inference in threshold using Gibbs sampling DA Sorensen"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Bayesian inference in threshold using Gibbs sampling DA Sorensen | Genet Sei Evol 1995 27 229-249 Elsevier INRA 229 Original article Bayesian inference in threshold models using Gibbs sampling DA Sorensen1 s Andersen2 D Gianola3 I Korsgaard1 1 National Institute of Animal Science Research Centre Foulum PO Box 39 DK-8830 Tjele 2 National Committee for Pig Breeding Health and Production Axeltorv 3 Copenhagen V Denmark 3 University of Wisconsin-Madison Department of Meat and Animal Sciences Madison WI 53706-1284 USA Received 17 June 1994 accepted 21 December 1994 Summary - A Bayesian analysis of a threshold model with multiple ordered categories is presented. Marginalizations are achieved by means of the Gibbs sampler. It is shown that use of data augmentation leads to conditional posterior distributions which are easy to sample from. The conditional posterior distributions of thresholds and liabilities are independent uniforms and independent truncated normals respectively. The remaining parameters of the model have conditional posterior distributions which are identical to those in the Gaussian linear model. The methodology is illustrated using a sire model with an analysis of hip dysplasia in dogs and the results are compared with those obtained in a previous study based on approximate maximum likelihood. Two independent Gibbs chains of length 620 000 each were run and the Monte-Carlo sampling error of moments of posterior densities were assessed using time series methods. Differences between results obtained from both chains were within the range of the Monte-Carlo sampling error. With the exception of the sire variance and heritability marginal posterior distributions seemed normal. Hence inferences using the present method were in good agreement with those based on approximate maximum likelihood. Threshold estimates were strongly autocorrelated in the Gibbs sequence but this can be alleviated using an alternative parameterization. threshold model Bayesian analysis Gibbs sampling dog Resume Inference bayésienne dans les modèles

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