Daniel Simpson
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Penalising model component complexity: A principled, practical approach to constructing priors
D Simpson, H Rue, A Riebler, TG Martins, SH Sørbye
Statistical science 32 (1), 1-28, 2017
Bayesian computing with INLA: new features
TG Martins, D Simpson, F Lindgren, H Rue
Computational Statistics & Data Analysis 67, 68-83, 2013
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
Visualization in Bayesian workflow
J Gabry, D Simpson, A Vehtari, M Betancourt, A Gelman
Journal of the Royal Statistical Society: Series A (Statistics in Society …, 2019
Spatio-temporal modelling of particulate matter concentration through the SPDE approach
M Cameletti, F Lindgren, DP Simpson, H Rue
AStA Adv Stat Anal, Submitted, 2011
The prior can often only be understood in the context of the likelihood
A Gelman, D Simpson, M Betancourt
Entropy 19 (10), 555, 2017
Rank-normalization, folding, and localization: An improved for assessing convergence of MCMC
A Vehtari, A Gelman, D Simpson, B Carpenter, PC Bürkner
arXiv preprint arXiv:1903.08008, 2019
Using stacking to average Bayesian predictive distributions (with discussion)
Y Yao, A Vehtari, D Simpson, A Gelman
Bayesian Analysis 13 (3), 917-1007, 2018
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
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
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
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
Pareto smoothed importance sampling
A Vehtari, D Simpson, A Gelman, Y Yao, J Gabry
arXiv preprint arXiv:1507.02646, 2015
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
On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods
AM Lyne, M Girolami, Y Atchadé, H Strathmann, D Simpson
Statistical science 30 (4), 443-467, 2015
Think continuous: Markovian Gaussian models in spatial statistics
D Simpson, F Lindgren, H Rue
Spatial Statistics 1, 16-29, 2012
Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment
G Shaddick, ML Thomas, H Amini, D Broday, A Cohen, J Frostad, A Green, ...
Environmental science & technology 52 (16), 9069-9078, 2018
In order to make spatial statistics computationally feasible, we need to forget about the covariance function
D Simpson, F Lindgren, H Rue
Environmetrics, 2010
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
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 0.0-1417182342, 2014
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