63 citations · 227 across the 34 of their papers we have counts for
9 papers · 1 filter
Integrated Nested Laplace Approximations for Large-Scale Spatial-Temporal Bayesian Modeling
Lisa Gaedke-Merzhäuser, Elias Krainski, Radim Janalik +2
Bayesian inference tasks continue to pose a computational challenge. This especially holds for spatial-temporal modeling where high-dimensional latent parameter spaces are ubiquito…
Parallelized integrated nested Laplace approximations for fast Bayesian inference
Lisa Gaedke-Merzhäuser, Janet van Niekerk, Olaf Schenk +1
There is a growing demand for performing larger-scale Bayesian inference tasks, arising from greater data availability and higher-dimensional model parameter spaces. In this work w…
Joint Posterior Inference for Latent Gaussian Models with R-INLA
Cristian Chiuchiolo, Janet van Niekerk, Haavard Rue
Efficient Bayesian inference remains a computational challenge in hierarchical models. Simulation-based approaches such as Markov Chain Monte Carlo methods are still popular but ha…
Importance Sampling with the Integrated Nested Laplace Approximation
Martin Outzen Berild, Sara Martino, Virgilio Gómez-Rubio +1
The Integrated Nested Laplace Approximation (INLA) is a deterministic approach to Bayesian inference on latent Gaussian models (LGMs) and focuses on fast and accurate approximation…
A Novel Method of Marginalisation using Low Discrepancy Sequences for Integrated Nested Laplace Approximations
Paul T. Brown, Chaitanya Joshi, Stephen Joe +1
Recently, it has been shown that approximations to marginal posterior distributions obtained using a low discrepancy sequence (LDS) can outperform standard grid-based methods with…
Markov Chain Monte Carlo with the Integrated Nested Laplace Approximation
Virgilio Gómez-Rubio, Håvard Rue
The Integrated Nested Laplace Approximation (INLA) has established itself as a widely used method for approximate inference on Bayesian hierarchical models which can be represented…