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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
7072017
Bayesian computing with INLA: new features
TG Martins, D Simpson, F Lindgren, H Rue
Computational Statistics & Data Analysis 67, 68-83, 2013
4992013
Visualization in Bayesian workflow
J Gabry, D Simpson, A Vehtari, M Betancourt, A Gelman
arXiv preprint arXiv:1709.01449, 2017
4772017
Bayesian computing with INLA: a review
H Rue, A Riebler, SH Sørbye, JB Illian, DP Simpson, FK Lindgren
arXiv preprint arXiv:1604.00860, 2016
4342016
Rank-normalization, folding, and localization: an improved R for assessing convergence of MCMC (with discussion)
A Vehtari, A Gelman, D Simpson, B Carpenter, PC Bürkner
Bayesian analysis 16 (2), 667-718, 2021
4152021
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
3472011
The prior can often only be understood in the context of the likelihood
A Gelman, D Simpson, M Betancourt
Entropy 19 (10), 555, 2017
3342017
Using stacking to average Bayesian predictive distributions (with discussion)
Y Yao, A Vehtari, D Simpson, A Gelman
Bayesian Analysis 13 (3), 917-1007, 2018
3252018
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
267*2019
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
2492016
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
240*2018
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
2112016
Pareto smoothed importance sampling
A Vehtari, D Simpson, A Gelman, Y Yao, J Gabry
arXiv preprint arXiv:1507.02646, 2015
2052015
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
1842018
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
1362018
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
121*2015
Validating Bayesian inference algorithms with simulation-based calibration
S Talts, M Betancourt, D Simpson, A Vehtari, A Gelman
arXiv preprint arXiv:1804.06788, 2018
1142018
Yes, but did it work?: Evaluating variational inference
Y Yao, A Vehtari, D Simpson, A Gelman
International Conference on Machine Learning, 5581-5590, 2018
1132018
Think continuous: Markovian Gaussian models in spatial statistics
D Simpson, F Lindgren, H Rue
Spatial Statistics 1, 16-29, 2012
1122012
In order to make spatial statistics computationally feasible, we need to forget about the covariance function
D Simpson, F Lindgren, H Rue
Environmetrics, 2010
1122010
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Artikler 1–20