19 citations · 31 across the 6 of their papers we have counts for
6 papers
PyINLA: Fast Bayesian Inference for Latent Gaussian Models in Python
Esmail Abdul Fattah, Elias Krainski, Havard Rue
Bayesian inference often relies on Markov chain Monte Carlo (MCMC) methods, particularly required for non-Gaussian data families. When dealing with complex hierarchical models, the…
GPU-Accelerated Parallel Selected Inversion for Structured Matrices Using sTiles
Esmail Abdul Fattah, Hatem Ltaief, Havard Rue +1
Selected inversion is essential for applications such as Bayesian inference, electronic structure calculations, and inverse covariance estimation, where computing only specific ele…
Constructing Priors that Penalize the Complexity of Gaussian Random Fields
Geir-Arne Fuglstad, Daniel Simpson, Finn Lindgren +1
Priors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides…
Multivariate Gaussian Random Fields Using Systems of Stochastic Partial Differential Equations
Xiangping Hu, Daniel Simpson, Finn Lindgren +1
In this paper a new approach for constructing \emph{multivariate} Gaussian random fields (GRFs) using systems of stochastic partial differential equations (SPDEs) has been introduc…
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…