Finn Lindgren
Statistics and Probability · 2011–2020
A researcher in the Science corpus: 15 papers; trusts 61 researchers and is trusted by 62 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 107 citations into it; #1,424 of 12,932 by trust here, #2,579 by citations
- Environmental Engineering — 96 citations into it; #84 of 5,479 by trust here, #297 by citations
- Artificial Intelligence — 71 citations into it; #5,268 of 35,345 by trust here, #12,083 by citations
- Economics and Econometrics — 58 citations into it; #2,000 of 23,481 by trust here, #3,565 by citations
- Global and Planetary Change — 26 citations into it; #3,343 of 10,225 by trust here, #7,711 by citations
- Applied Mathematics — 22 citations into it; #915 of 7,620 by trust here, #732 by citations
- Management Science and Operations Research — 16 citations into it
- Signal Processing — 11 citations into it
Whom Finn Lindgren trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Håvard Rue
- Julian Besag
- David Bolin
- Noel Cressie
- Sara Martino
- Sudipto Banerjee
- Michael L. Stein
- Tilmann Gneiting
- Deepayan Sarkar
- Christian P. Robert
Who trusts Finn Lindgren overall
- Håvard Rue
- Francky Fouedjio
- R. Ingebrigtsen
- Daniel P. Simpson
- H. Bakka
- Noel Cressie
- Geir-Arne Fuglstad
- Andrea Riebler
- Jonathan Koh
- J. Thorson
Papers from Finn Lindgren's perspective
Ordered by how strongly the researchers Finn Lindgren trusts endorse each paper. Same corpus, a different order for every researcher.
- Spatial Interaction and the Statistical Analysis of Lattice Systems
- Gaussian Markov Random Fields: Theory and Applications
- Bayesian measures of model complexity and fit
- Hierarchical Modeling and Analysis for Spatial Data
- Applied Regression Including Computing and Graphics
- An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach
- Interpolation of spatial data
- Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
- Nonparametric Estimation of Nonstationary Spatial Covariance Structure
- Spatial modelling using a new class of nonstationary covariance functions (Environmetrics)
Their papers
- Bayesian Computing with INLA: A Review (2016) — 746 citations
- Bayesian Spatial Modelling with R-INLA (2015) — 1,259 citations
- Bayesian computing with INLA: New features (2012) — 626 citations
- An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach (2011) — 2,822 citations
- Spatial models with explanatory variables in the dependence structure (2014) — 113 citations
- Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping (2011) — 142 citations
- Excursion and contour uncertainty regions for latent Gaussian models (2012) — 138 citations
- Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field (2014) — 47 citations
- Spatial modelling with R-INLA: A review (2018) — 401 citations
- A Case Study Competition Among Methods for Analyzing Large Spatial Data (2017) — 458 citations
- Probabilistic Interpolation of Uncertain Local Activation Times on Human Atrial Manifolds (2020) — 26 citations
- The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015 (2015) — 2,784 citations
- A Multiresolution Gaussian Process Model for the Analysis of Large Spatial Datasets (2015) — 353 citations
- Constructing Priors that Penalize the Complexity of Gaussian Random Fields (2015) — 413 citations
- Spatio-temporal modeling of particulate matter concentration through the SPDE approach (2012) — 406 citations