Modelling and Inference for Bayesian Bivariate Animal Models using Integrated Nested Laplace Approximations

Abstract

In this study we focus on performing inference on bivariate animal models using Integrated Nested Laplace Approximation (INLA). INLA is a methodology for making fast non-sampling based Bayesian inference for hierarchical Gaussian Markov models. Animal models are generalized mixed models (GLMM) used in evolutionary biology and animal breeding to identify the genetic part of traits. Bivariate animal models are derived and shown to fit the INLA framework. Simulation studies are conducted to evaluate the performance of the models. The models are fitted to a real data set of Scots pine to investigate correlations and dependencies.