title: Utilizing Gaussian Markov random field properties of Bayesian animal models.
abstract: In this article, we demonstrate how Gaussian Markov random field properties give large computational benefits and new opportunities for the Bayesian animal model. We make inference by computing the posteriors for important quantitative genetic variables. For the single-trait animal model, a nonsampling-based approximation is presented. For the multitrait model, we set up a robust and fast Markov chain Monte Carlo algorithm. The proposed methodology was used to analyze quantitative genetic properties of morphological traits of a wild house sparrow population. Results for single- and multitrait models were compared.
authors.affiliation: Department of Mathematical Sciences, NTNU, Trondheim, Norway. ingelins@math.ntnu.no
authors.forename: Ingelin
authors.initials: I
authors.lastname: Steinsland
authors.forename: Henrik
authors.initials: H
authors.lastname: Jensen
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D055614:Genetic Phenomena; D008390:Markov Chains; D023421:Models, Animal; D009010:Monte Carlo Method; D046370:Sparrows