Animal Models and Integrated Nested Laplace Approximations
نویسندگان
چکیده
Animal models are generalized linear mixed models used in evolutionary biology and animal breeding to identify the genetic part of traits. Integrated Nested Laplace Approximation (INLA) is a methodology for making fast, nonsampling-based Bayesian inference for hierarchical Gaussian Markov models. In this article, we demonstrate that the INLA methodology can be used for many versions of Bayesian animal models. We analyze animal models for both synthetic case studies and house sparrow (Passer domesticus) population case studies with Gaussian, binomial, and Poisson likelihoods using INLA. Inference results are compared with results using Markov Chain Monte Carlo methods. For model choice we use difference in deviance information criteria (DIC). We suggest and show how to evaluate differences in DIC by comparing them with sampling results from simulation studies. We also introduce an R package, AnimalINLA, for easy and fast inference for Bayesian Animal models using INLA.
منابع مشابه
NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET Animal models and Integrated Nested Laplace Approximations
Animal models are generalized linear mixed model (GLMM) used in evolutionary biology and animal breeding to identify the genetic part of traits. Integrated Nested Laplace Approximation (INLA) is a methodology for making fast non-sampling based Bayesian inference for hierarchical Gaussian Markov models. In this paper we demonstrate that the INLA methodology can be used for many versions of Bayes...
متن کاملBayesian nonparametric regression and density estimation using integrated nested Laplace approximations.
Integrated nested Laplace approximations (INLA) are a recently proposed approximate Bayesian approach to fit structured additive regression models with latent Gaussian field. INLA method, as an alternative to Markov chain Monte Carlo techniques, provides accurate approximations to estimate posterior marginals and avoid time-consuming sampling. We show here that two classical nonparametric smoot...
متن کاملAn analysis of Japanese liver cancer mortality data with Bayesian age–period–cohort models
Age–period–cohort (APC) models have been widely used in the analysis of incidence and mortality data. Bayesian APC models, in which multivariate Gaussian priors are incorporated on age, period and cohort effects, can evade the identifiability problem. Inference with integrated nested Laplace approximations (INLA) has recently been a useful tool. An application of the Bayesian APC models with IN...
متن کاملDiscussion on “ Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
integrated nested Laplace approximations” by H. Rue, S. Martino, and N. Chopin, Christian P. Robert, CEREMADE, Université Paris Dauphine and CREST, INSEE Rue, Martino and Chopin are to be congratulated on their impressive and wide-ranging attempt at overcoming the difficulties in handling latent Gaussian structures. In time series as well as spatial problems, the explosion in the dimension of t...
متن کاملApproximate Bayesian Inference for Latent Gaussian Models Using Integrated Nested Laplace Approximations
Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalised) linear models, (generalised) additive models, smoothing-spline models, state-space models, semiparametric regression, spatial and spatio-temporal models, log-Gaussian Cox-processes, and geostatistical models. In this paper we consider app...
متن کامل