Publications (29)
Computationally efficient spatial modeling of annual maximum 24 hour precipitation. An application to data from Iceland
Ãli Páll Geirsson, Birgir Hrafnkelsson, Daniel Simpson
Discussion of "Sequential Quasi-Monte Carlo" by Mathieu Gerber and Nicolas Chopin
Chris. J. Oates, Daniel Simpson, Mark Girolami
The use of systems of stochastic PDEs as priors for multivariate models with discrete structures
Erlend Aune, Daniel Simpson
Using stacking to average Bayesian predictive distributions
Yuling Yao, Aki Vehtari, Daniel Simpson +1
The experiment is just as important as the likelihood in understanding the prior: A cautionary note on robust cognitive modelling
Lauren Kennedy, Daniel Simpson, Andrew Gelman
The MCMC split sampler: A block Gibbs sampling scheme for latent Gaussian models
Ãli Páll Geirsson, Birgir Hrafnkelsson, Daniel Simpson +1
Fast approximate inference with INLA: the past, the present and the future
Daniel Simpson, Finn Lindgren, HÃ¥vard Rue
Think continuous: Markovian Gaussian models in spatial statistics
Daniel Simpson, Finn Lindgren, HÃ¥vard Rue
Multivariate Gaussian Random Fields Using Systems of Stochastic Partial Differential Equations
Xiangping Hu, Daniel Simpson, Finn Lindgren +1
The prior can generally only be understood in the context of the likelihood
Andrew Gelman, Daniel Simpson, Michael Betancourt
Going off grid: Computationally efficient inference for log-Gaussian Cox processes
Daniel Simpson, Janine Illian, Finn Lindgren +2
On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods
Anne-Marie Lyne, Mark Girolami, Yves Atchadé +2
Specifying Gaussian Markov Random Fields with Incomplete Orthogonal Factorization using Givens Rotations
Xiangping Hu, Daniel Simpson, HÃ¥vard Rue
Bayesian Adaptive Smoothing Spline using Stochastic Differential Equations
Yu Ryan Yue, Daniel Simpson, Finn Lindgren +1
Non-stationary Gaussian models with physical barriers
Haakon Bakka, Jarno Vanhatalo, Janine Illian +2
Beyond the Valley of the Covariance Function
Daniel Simpson, Finn Lindgren, HÃ¥vard Rue
Spatial modelling with R-INLA: A review
Haakon Bakka, HÃ¥vard Rue, Geir-Arne Fuglstad +5
Constructing Priors that Penalize the Complexity of Gaussian Random Fields
Geir-Arne Fuglstad, Daniel Simpson, Finn Lindgren +1
Exploring a New Class of Non-stationary Spatial Gaussian Random Fields with Varying Local Anisotropy
Geir-Arne Fuglstad, Finn Lindgren, Daniel Simpson +1
Visualization in Bayesian workflow
Jonah Gabry, Daniel Simpson, Aki Vehtari +2
Does non-stationary spatial data always require non-stationary random fields?
Geir-Arne Fuglstad, Daniel Simpson, Finn Lindgren +1
Yes, but Did It Work?: Evaluating Variational Inference
Yuling Yao, Aki Vehtari, Daniel Simpson +1
An intuitive Bayesian spatial model for disease mapping that accounts for scaling
Andrea Riebler, Sigrunn H. Sørbye, Daniel Simpson +1
Bayesian computing with INLA: new features
Thiago G. Martins, Daniel Simpson, Finn Lindgren +1
Non-stationary Spatial Modelling with Applications to Spatial Prediction of Precipitation
Geir-Arne Fuglstad, Daniel Simpson, Finn Lindgren +1
Multivariate Gaussian Random Fields with Oscillating Covariance Functions using Systems of Stochastic Partial Differential Equations
Xiangping Hu, Finn Lindgren, Daniel Simpson +1
Spatial Modeling, with Application to Complex Survey Data: Discussion of "Model-based Geostatistics for Prevalence Mapping in Low-Resource Settings", by Diggle and Giorgi
Jon Wakefield, Daniel Simpson, Jessica Godwin
Discussion of "Geodesic Monte Carlo on Embedded Manifolds"
Simon Byrne, Mark Girolami, Persi Diaconis +9
Spatial Modelling of Temperature and Humidity using Systems of Stochastic Partial Differential Equations
Xiangping Hu, Ingelin Steinsland, Daniel Simpson +2