Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations H Rue, S Martino, N Chopin Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2009 | 5598 | 2009 |
Gaussian Markov random fields: theory and applications H Rue, L Held Chapman and Hall/CRC, 2005 | 3536 | 2005 |
An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach F Lindgren, H Rue, J Lindström Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2011 | 2833 | 2011 |
Bayesian spatial modelling with R-INLA F Lindgren, H Rue Journal of statistical software 63 (19), 2015 | 1207 | 2015 |
Penalising model component complexity: A principled, practical approach to constructing priors D Simpson, H Rue, A Riebler, TG Martins, SH Sørbye | 1110 | 2017 |
Bayesian computing with INLA: a review H Rue, A Riebler, SH Sørbye, JB Illian, DP Simpson, FK Lindgren Annual Review of Statistics and Its Application 4 (1), 395-421, 2017 | 704 | 2017 |
Spatial and spatio-temporal models with R-INLA M Blangiardo, M Cameletti, G Baio, H Rue Spatial and spatio-temporal epidemiology 4, 33-49, 2013 | 667 | 2013 |
Bayesian computing with INLA: new features TG Martins, D Simpson, F Lindgren, H Rue Computational Statistics & Data Analysis 67, 68-83, 2013 | 648 | 2013 |
Fast sampling of Gaussian Markov random fields H Rue Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2001 | 545 | 2001 |
An intuitive Bayesian spatial model for disease mapping that accounts for scaling A Riebler, SH Sørbye, D Simpson, H Rue Statistical methods in medical research 25 (4), 1145-1165, 2016 | 426 | 2016 |
Spatio-temporal modeling of particulate matter concentration through the SPDE approach M Cameletti, F Lindgren, D Simpson, H Rue AStA Advances in Statistical Analysis 97, 109-131, 2013 | 413 | 2013 |
Constructing priors that penalize the complexity of Gaussian random fields GA Fuglstad, D Simpson, F Lindgren, H Rue Journal of the American Statistical Association 114 (525), 445-452, 2019 | 391 | 2019 |
Spatial modeling with R‐INLA: A review H Bakka, H Rue, GA Fuglstad, A Riebler, D Bolin, J Illian, E Krainski, ... Wiley Interdisciplinary Reviews: Computational Statistics 10 (6), e1443, 2018 | 351 | 2018 |
Focus on Sport: Prediction and Retrospective Analysis of Soccer Matches in a League H Rue, Ø Salvesen Journal of the Royal Statistical Society Series D: The Statistician 49 (3 …, 2000 | 351 | 2000 |
Bayesian inference for generalized linear mixed models Y Fong, H Rue, J Wakefield Biostatistics 11 (3), 397-412, 2010 | 349 | 2010 |
Fitting Gaussian Markov random fields to Gaussian fields H Rue, H Tjelmeland Scandinavian journal of Statistics 29 (1), 31-49, 2002 | 343 | 2002 |
Advanced spatial modeling with stochastic partial differential equations using R and INLA E Krainski, V Gómez-Rubio, H Bakka, A Lenzi, D Castro-Camilo, ... Chapman and Hall/CRC, 2018 | 306 | 2018 |
Approximate Bayesian inference for hierarchical Gaussian Markov random field models H Rue, S Martino Journal of statistical planning and inference 137 (10), 3177-3192, 2007 | 299 | 2007 |
On block updating in Markov random field models for disease mapping L Knorr‐Held, H Rue Scandinavian Journal of Statistics 29 (4), 597-614, 2002 | 287 | 2002 |
Going off grid: Computationally efficient inference for log-Gaussian Cox processes D Simpson, JB Illian, F Lindgren, SH Sørbye, H Rue Biometrika 103 (1), 49-70, 2016 | 262 | 2016 |