NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET Making Inference from Bayesian Animal Models utilising Gaussian Markov Random Field properties
نویسندگان
چکیده
Numerical efficient methods for sampling and evaluation of Gaussian Markov Random Fields (GMRFs) are used for making inference from Bayesian animal models (also known as additive genetic models, that are versions of general linear models). For single-trait animal models an approximation to the posterior distribution of variance components and the heritability can be found without using Markov chain Monte Carlo (MCMC) methods. For the multiple-trait animal model a two-block Gibbs sampler can be used, also for large datasets. The above methodology is successfully used to study the genetic architecture of morphological traits in a house sparrow meta-population. The pedigree consists of 3572 birds and there are data for seven traits, i.e. the Bayesian animal model has more that 25000 variables. The results provide strong indications of possibilities for, but also constraints on micro-evolution.
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 کاملNorges Teknisk-naturvitenskapelige Universitet Fitting Gaussian Markov Random Fields to Gaussian Fields Fitting Gaussian Markov Random Fields to Gaussian Fields Tmr Project on Spatial Statistics (erb-fmrx-ct960095) for Support and Inspiration
SUMMARY This paper discusses the following task often encountered building Bayesian spatial models: construct a homogeneous Gaussian Markov random field (GMRF) on a lattice with correlation properties either as present in observed data or consistent with prior knowledge. The Markov property is essential in design of computational efficient Markov chain Monte Carlo algorithms used to analyse suc...
متن کاملNORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET Approximate Bayesian Inference for Multivariate Stochastic Volatility Models
In this report we apply Integrated Nested Laplace approximation (INLA) to a series of multivariate stochastic volatility models. These are a useful construct in financial time series analysis and can be formulated as latent Gaussian Markov Random Field (GMRF) models. This popular class of models is characterised by a GMRF as the second stage of the hierarchical structure and a vector of hyperpa...
متن کاملNORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET Approximate Inference for Hierarchical Gaussian Markov Random Fields Models
Many commonly used models in statistics can be formulated as Hierarchical Gaussian Markov random field (GMRF) models. These are characterised by assuming a (often large) GMRF as the second stage in the hierarchical model and a few hyperparameters at the third stage. Markov chain Monte Carlo is the common approach to do inference from such models. The variance of the Monte Carlo estimates is Op(...
متن کاملNORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET Approximate Bayesian Inference for Survival Models
Bayesian analysis of time-to-event data, usually called survival analysis, has received increasing attention in the last years. In Cox-type models it allows to use information from the full likelihood instead of from a partial likelihood, so that the baseline hazard function and the model parameters can be jointly estimated. In general, Bayesian methods permit a full and exact posterior inferen...
متن کامل