2016/11/04 by Aliaksandr Hubin, Hubin, Aliaksandr, Geir Storvik +1
Computer Science · Mathematics · #62F15 #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1611.01450
openalex publication_date 2016/11/04 · openalex created_date 2016/11/30 · openalex updated_date 2026/07/28
The marginal likelihood is a well established model selection criterion in Bayesian statistics. It also allows to efficiently calculate the marginal posterior model probabilities that can be used for Bayesian model averaging of quantities of interest. For many complex models, including latent modeling approaches, marginal likelihoods are however difficult to compute. One recent promising approach for approximating the marginal likelihood is Integrated Nested Laplace Approximation (INLA), design for models with latent Gaussian structures. In this study we compare the approximations obtained with INLA to some alternative approaches on a number of examples of different complexity. In particular we address a simple linear latent model, a Bayesian linear regression model, logistic Bayesian regression models with probit and logit links, and a Poisson longitudinal generalized linear mixed model.