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Báo cáo khoa hoc:" A useful reparameterisation to obtain samples from conditional inverse"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: A useful reparameterisation to obtain samples from conditional inverse | 177 Genet. Sei. Evol. 31 1999 177 181 Inra Elsevier Paris Note A useful reparameterisation to obtain samples from conditional inverse Wishart distributions Inge Riis Korsgaard Anders Holst Andersenb Daniel Sorensena a Department of Animal Breeding and Genetics Research Centre Foulum PO Box 50 DK-8830 Tjele Denmark b Department of Theoretical Statistics University of Aarhus DK-8000 Aarhus c Denmark Received 12 December 1997 accepted 6 January 1999 Abstract - A Bayesian joint analysis of normally distributed traits and binary traits using the Gibbs sampler requires the drawing of samples from a conditional inverse Wishart distribution. This is the fully conditional posterior distribution of the residual covariance matrix of the normally distributed traits and liabilities of the binary traits. Obtaining samples from the conditional inverse Wishart distribution is not straightforward. However combining well-known matrix results and properties of the Wishart distribution it is shown that this can be easily carried out by successively drawing from Wishart and normally distributed random variables. Inra Elsevier Paris conditional inverse Wishart distribution Gibbs sampling binary traits residual covariance matrix Resume Reparamétrisation permettant d obtenir des échantillons tirés d une loi de Wishart inverse conditionnée. Une analyse bayésienne utilisant 1 échantillon-nage de Gibbs de caractères distribués normalement conjointeinent avec des carac-tères binaires requiert le tirage d echantillons dans une loi de Wishart inverse conditionnée. Il s agit de la distribution a posteriori de la matrice de covariance residuelie des caractères distribués normalement et des variables latentes corres-pondant aux variables binaires. L obtention d echantillons correspondants n est pas évidente. Cependant I utilisation de résultats bien connus sur les matrices et des propriétés de la distribution de Wishart permet d aboutir à une solution en tirant Correspondence and reprints _