Daniel Simpson
Daniel Simpson
Normal Computing
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Cited by
Cited by
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
Penalising model component complexity: A principled, practical approach to constructing priors
D Simpson, H Rue, A Riebler, TG Martins, SH Sørbye
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 182 …, 2019
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
Bayesian computing with INLA: new features
TG Martins, D Simpson, F Lindgren, H Rue
Computational Statistics & Data Analysis 67, 68-83, 2013
Using stacking to average Bayesian predictive distributions (with discussion)
Y Yao, A Vehtari, D Simpson, A Gelman
Bayesian Analysis 13 (3), 917-1003, 2018
The prior can often only be understood in the context of the likelihood
A Gelman, D Simpson, M Betancourt
Entropy 19 (10), 555, 2017
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
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
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
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
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
Pareto smoothed importance sampling
A Vehtari, D Simpson, A Gelman, Y Yao, J Gabry
arXiv preprint arXiv:1507.02646, 2015
Bayesian workflow
A Gelman, A Vehtari, D Simpson, CC Margossian, B Carpenter, Y Yao, ...
arXiv preprint arXiv:2011.01808, 2020
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
Validating Bayesian inference algorithms with simulation-based calibration
S Talts, M Betancourt, D Simpson, A Vehtari, A Gelman
arXiv preprint arXiv:1804.06788, 2018
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
Yes, but did it work?: Evaluating variational inference
Y Yao, A Vehtari, D Simpson, A Gelman
International Conference on Machine Learning, 5581-5590, 2018
Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan
M Morris, K Wheeler-Martin, D Simpson, SJ Mooney, A Gelman, ...
Spatial and spatio-temporal epidemiology 31, 100301, 2019
On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods
AM Lyne, M Girolami, Y Atchadé, H Strathmann, D Simpson
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