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 | 1125 | 2011 |

The effect of malaria control on *Plasmodium falciparum* in Africa between 2000 and 2015S Bhatt, DJ Weiss, E Cameron, D Bisanzio, B Mappin, U Dalrymple, ... Nature 526 (7572), 207, 2015 | 1015 | 2015 |

Bayesian spatial modelling with R-INLA F Lindgren, H Rue Journal of Statistical Software 63 (19), 1-25, 2015 | 282 | 2015 |

Bayesian computing with INLA: new features TG Martins, D Simpson, F Lindgren, H Rue Computational Statistics & Data Analysis 67, 68-83, 2013 | 257 | 2013 |

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 (2), 109-131, 2013 | 214 | 2013 |

A multiresolution Gaussian process model for the analysis of large spatial datasets D Nychka, S Bandyopadhyay, D Hammerling, F Lindgren, S Sain Journal of Computational and Graphical Statistics 24 (2), 579-599, 2015 | 140 | 2015 |

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, 395-421, 2017 | 118 | 2017 |

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 | 109 | 2016 |

Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping D Bolin, F Lindgren The Annals of Applied Statistics 5 (1), 523-550, 2011 | 103 | 2011 |

Think continuous: Markovian Gaussian models in spatial statistics D Simpson, F Lindgren, H Rue Spatial Statistics 1, 16-29, 2012 | 83 | 2012 |

In order to make spatial statistics computationally feasible, we need to forget about the covariance function D Simpson, F Lindgren, H Rue Environmetrics 23 (1), 65-74, 2012 | 74 | 2012 |

INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion H Rue, S Martino, F Lindgren, D Simpson, A Riebler, ET Krainski R package version 0.0-1404466487, URL http://www. R-INLA. org, 2014 | 71 | 2014 |

Excursion and contour uncertainty regions for latent Gaussian models D Bolin, F Lindgren Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2015 | 63 | 2015 |

On the second‐order random walk model for irregular locations F Lindgren, H Rue Scandinavian journal of statistics 35 (4), 691-700, 2008 | 60 | 2008 |

Spatial models with explanatory variables in the dependence structure R Ingebrigtsen, F Lindgren, I Steinsland Spatial Statistics 8, 20-38, 2014 | 52 | 2014 |

Exploring a new class of non-stationary spatial Gaussian random fields with varying local anisotropy GA Fuglstad, F Lindgren, D Simpson, H Rue Statistica Sinica, 115-133, 2015 | 50 | 2015 |

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 | 49 | 2019 |

INLA: functions which allow to perform a full Bayesian analysis of structured additive models using Integrated Nested Laplace Approximation H Rue, S Martino, F Lindgren, D Simpson, A Riebler, ET Krainski R package version 0.0, 2009 | 49 | 2009 |

Does non-stationary spatial data always require non-stationary random fields? GA Fuglstad, D Simpson, F Lindgren, H Rue Spatial Statistics 14, 505-531, 2015 | 38 | 2015 |

Analyzing the image warp forecast verification method on precipitation fields from the ICP E Gilleland, J Lindström, F Lindgren Weather and Forecasting 25 (4), 1249-1262, 2010 | 34 | 2010 |