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Scalable iterative methods for sampling from massive Gaussian random vectors
Daniel P. Simpson, Ian W. Turner, Christopher M. Strickland +1
Sampling from Gaussian Markov random fields (GMRFs), that is multivariate Gaussian ran- dom vectors that are parameterised by the inverse of their covariance matrix, is a fundament…
Specifying Gaussian Markov Random Fields with Incomplete Orthogonal Factorization using Givens Rotations
Xiangping Hu, Daniel Simpson, Håvard Rue
In this paper an approach for finding a sparse incomplete Cholesky factor through an incomplete orthogonal factorization with Givens rotations is discussed and applied to Gaussian…
Non-stationary Spatial Modelling with Applications to Spatial Prediction of Precipitation
Geir-Arne Fuglstad, Daniel Simpson, Finn Lindgren +1
A non-stationary spatial Gaussian random field (GRF) is described as the solution of an inhomogeneous stochastic partial differential equation (SPDE), where the covariance structur…
On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods
Anne-Marie Lyne, Mark Girolami, Yves Atchadé +2
A large number of statistical models are "doubly-intractable": the likelihood normalising term, which is a function of the model parameters, is intractable, as well as the marginal…
Exploring a New Class of Non-stationary Spatial Gaussian Random Fields with Varying Local Anisotropy
Geir-Arne Fuglstad, Finn Lindgren, Daniel Simpson +1
Gaussian random fields (GRFs) constitute an important part of spatial modelling, but can be computationally infeasible for general covariance structures. An efficient approach is t…