Geir-Arne Fuglstad
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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
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
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
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
Assessing comorbidity and correlates of wasting and stunting among children in Somalia using cross-sectional household surveys: 2007 to 2010
DK Kinyoki, NB Kandala, SO Manda, ET Krainski, GA Fuglstad, ...
Bmj Open 6 (3), e009854, 2016
Estimating under-five mortality in space and time in a developing world context
J Wakefield, GA Fuglstad, A Riebler, J Godwin, K Wilson, SJ Clark
Statistical methods in medical research 28 (9), 2614-2634, 2019
Predicting soil properties in the Canadian boreal forest with limited data: Comparison of spatial and non-spatial statistical approaches
J Beguin, GA Fuglstad, N Mansuy, D Paré
Geoderma 306, 195-205, 2017
Predominant regional biophysical cooling from recent land cover changes in Europe
B Huang, X Hu, GA Fuglstad, X Zhou, W Zhao, F Cherubini
Nature communications 11 (1), 1-13, 2020
Landscape relatedness: detecting contemporary fine-scale spatial structure in wild populations
AJ Norman, AV Stronen, GA Fuglstad, A Ruiz-Gonzalez, J Kindberg, ...
Landscape Ecology 32 (1), 181-194, 2017
Intuitive joint priors for variance parameters
GA Fuglstad, IG Hem, A Knight, H Rue, A Riebler
Bayesian Analysis 15 (4), 1109-1137, 2020
Spatial modelling and inference with spde-based gmrfs
GA Fuglstad
Institutt for matematiske fag, 2011
Environmental mapping using Bayesian spatial modelling (INLA/SPDE): A reply to Huang et al.(2017)
GA Fuglstad, J Beguin
Science of The Total Environment 624, 596-598, 2018
Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge
IG Hem, ML Selle, G Gorjanc, GA Fuglstad, A Riebler
Genetics 217 (3), iyab002, 2021
Design-and model-based approaches to small-area estimation in a low and middle income country context: comparisons and recommendations
J Paige, GA Fuglstad, A Riebler, J Wakefield
arXiv preprint arXiv:1910.06512, 2019
INLA: Full Bayesian analysis of latent Gaussian models using integrated nested Laplace approximations
H Rue, F Lindgren, D Simpson, S Martino, E Teixeira Krainski, H Bakka, ...
R package version 19 (03), 2019
Approximating Solutions of Stochastic Differential Equations with Gaussian Markov Random Fields
GA Fuglstad
Pre-Master’s thesis, Department of Mathematical Sciences, NTNU, 2010
Compression of climate simulations with a nonstationary global SpatioTemporal SPDE model
GA Fuglstad, S Castruccio
The Annals of Applied Statistics 14 (2), 542-559, 2020
Modelling Spatial non-stationarity
GA Fuglstad
NTNU, 2015
makemyprior: Intuitive Construction of Joint Priors for Variance Parameters in R
IG Hem, GA Fuglstad, A Riebler
arXiv preprint arXiv:2105.09712, 2021
Space-Time Smoothing of Demographic and Health Indicators using the R Package SUMMER
ZR Li, BD Martin, TQ Dong, GA Fuglstad, J Paige, A Riebler, S Clark, ...
arXiv preprint arXiv:2007.05117, 2020
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