TAILIEUCHUNG - Báo cáo sinh học: "Bayesian inference about dispersion parameters of univariate mixed models with maternal effects: theoretical considerations"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Bayesian inference about dispersion parameters of univariate mixed models with maternal effects: theoretical considerations | Genet Sei Evol 1992 24 107-135 Elsevier INRA 107 Original article Bayesian inference about dispersion parameters of univariate mixed models with maternal effects theoretical considerations RJC Cantet RL Fernando D Gianola University of Illinois Department of Animal Sciences Urbana IL 61801 USA Received 14 January 1991 accepted 5 January 1992 Summary - Mixed linear models for maternal effects include fixed and random elements and dispersion parameters variances and covariances . In this paper a Bayesian model for inferences about such parameters is presented. The model includes a normal likelihood for the data a flat prior for the fixed effects and a multivariate normal prior for the direct and maternal breeding values. The prior distribution for the genetic variancecovariance components is in the inverted Wishart form and the environmental components follow inverted X2 prior distributions. The kernel of the joint posterior density of the dispersion parameters is derived in closed form. Additional numerical and analytical methods of interest that are suggested to complete a Bayesian analysis include MonteCarlo Integration maximum entropy fit asymptotic approximations and the Tierney-Kadane approach to marginalization. maternal effect Bayesian method dispersion parameter Resume - Inference bayésienne des paramètres de dispersion de modèles mixtes univariates avec effets maternels considerations théoriques. Les modèles linéaires mixtes avec effets matemels comprennent des elements fixes et aléatoires et des paramètres de dispersion variances et covariances . Dans cet article est présenté un modèle bayésien pour I estimation de ces paramètres. Le modèle comprend une vraisemblance normale pour les données un a priori uniforme pour les effets fixes et un a priori multivariate normal pour les valeurs génétiques directes et matemelles. La distribution a priori des composantes de variance-covariance est une distribution de Wishart inverse et les composantes de milieu _ .

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