S
Sara Martino
Researcher at Norwegian University of Science and Technology
Publications - 37
Citations - 5383
Sara Martino is an academic researcher from Norwegian University of Science and Technology. The author has contributed to research in topics: Bayesian inference & Laplace's method. The author has an hindex of 13, co-authored 31 publications receiving 4387 citations. Previous affiliations of Sara Martino include Stazione Zoologica Anton Dohrn & SINTEF.
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Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
TL;DR: This work considers approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non‐Gaussian response variables and can directly compute very accurate approximations to the posterior marginals.
Approximate Bayesian Inference for Latent Gaussian Models
TL;DR: The approximation tool for latent GMRF models is introduced and the approximation for the posterior of the hyperparameters θ in equation (1) is shown to give extremely accurate results in a fraction of the computing time used by MCMC algorithms.
Journal ArticleDOI
Approximate Bayesian inference for hierarchical Gaussian Markov random field models
Håvard Rue,Sara Martino +1 more
TL;DR: It is conjecture that for many hierarchical GMRF-models there is really no need for MCMC based inference to estimate marginal densities, and by making use of numerical methods for sparse matrices the computational costs of these deterministic schemes are nearly instant compared to the MCMC alternative.
Journal ArticleDOI
Approximate Bayesian Inference for Survival Models.
TL;DR: This article shows how a new inferential tool named integrated nested Laplace approximations can be adapted and applied to many survival models making Bayesian analysis both fast and accurate without having to rely on MCMC‐based inference.
Implementing Approximate Bayesian Inference using Integrated Nested Laplace Approximation: a manual for the inla program
Sara Martino,Håvard Rue +1 more
TL;DR: The approximation tool for latent GMRF models is introduced and the approximation for the posterior of the hyperparameters θ in equation (1) is shown to give extremely accurate results in a fraction of the computing time used by MCMC algorithms.