_:n299b451a1b6c48cf87e27db12d75df1fxb54
<http://schema.org/description>
"The Integrated Nested Laplace Approximation (INLA) is a deterministic\napproach to Bayesian inference on latent Gaussian models (LGMs) and focuses on\nfast and accurate approximation of posterior marginals for the parameters in\nthe models. Recently, methods have been developed to extend this class of\nmodels to those that can be expressed as conditional LGMs by fixing some of the\nparameters in the models to descriptive values. These methods differ in the\nmanner descriptive values are chosen. This paper proposes to combine importance\nsampling with INLA (IS-INLA), and extends this approach with the more robust\nadaptive multiple importance sampling algorithm combined with INLA (AMIS-INLA).\n  This paper gives a comparison between these approaches and existing methods\non a series of applications with simulated and observed datasets and evaluates\ntheir performance based on accuracy, efficiency, and robustness. The approaches\nare validated by exact posteriors in a simple bivariate linear model; then,\nthey are applied to a Bayesian lasso model, a Bayesian imputation of missing\ncovariate values, and lastly, in parametric Bayesian quantile regression. The\napplications show that the AMIS-INLA approach, in general, outperforms the\nother methods, but the IS-INLA algorithm could be considered for faster\ninference when good proposals are available."
<https://api.deepai.org/profile/virgilio-gomez-rubio>