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- Publications
- Influence

Gaussian Markov Random Fields: Theory and Applications

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics, a very active area of research in which few up-to-date reference works are available. Gaussian Markov Random… Expand

An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach

- F. Lindgren, H. Rue, J. Lindström
- Mathematics
- 1 September 2011

Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical modelling and geostatistics. The specification through the covariance function gives an intuitive… Expand

Fast sampling of Gaussian Markov random fields

- H. Rue
- Mathematics
- 2001

This paper demonstrates how Gaussian Markov random fields (conditional autoregressions) can be sampled quickly by using numerical techniques for sparse matrices. The algorithm is general and… Expand

Bayesian Spatial Modelling with R-INLA

- F. Lindgren, H. Rue
- Computer Science
- 16 February 2015

The principles behind the interface to continuous domain spatial models in the RINLA software package for R are described. The integrated nested Laplace approximation (INLA) approach proposed by Rue,… Expand

Bayesian computing with INLA: New features

- Thiago G. Martins, D. Simpson, F. Lindgren, H. Rue
- Mathematics, Computer Science
- Comput. Stat. Data Anal.
- 1 October 2012

The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable tool in practice… Expand

An explicit link between Gaussian fields and Gaussian Markov random fields; The SPDE approach

- F. Lindgren, J. Lindström, H. Rue
- Mathematics
- 2010

Continuously indexed Gaussian fields (GFs) is the most important ingredient in spatial statistical modelling and geo-statistics. The specification through the covariance function gives an intuitive… Expand

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Bayesian inference for generalized linear mixed models.

- Y. Fong, H. Rue, J. Wakefield
- Mathematics, Medicine
- Biostatistics
- 1 July 2010

Generalized linear mixed models (GLMMs) continue to grow in popularity due to their ability to directly acknowledge multiple levels of dependency and model different data types. For small sample… Expand

Spatio-temporal modeling of particulate matter concentration through the SPDE approach

- Michela Cameletti, F. Lindgren, D. Simpson, H. Rue
- Mathematics
- 1 April 2013

In this work, we consider a hierarchical spatio-temporal model for particulate matter (PM) concentration in the North-Italian region Piemonte. The model involves a Gaussian Field (GF), affected by a… Expand

On Block Updating in Markov Random Field Models for Disease Mapping

- L. Knorr-Held, H. Rue
- Mathematics
- 1 December 2002

Gaussian Markov random field (GMRF) models are commonly used to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly single-site updating… Expand