no code implementations • 25 Apr 2013 • Geir-Arne Fuglstad, Finn Lindgren, Daniel Simpson, Håvard Rue
This allows for the introduction of parameters that control the GRF by parametrizing the diffusion matrix.
Methodology
no code implementations • 2 Sep 2014 • Geir-Arne Fuglstad, Daniel Simpson, Finn Lindgren, Håvard Rue
A stationary spatial model is an idealization and we expect that the true dependence structures of physical phenomena are spatially varying, but how should we handle this non-stationarity in practice?
Methodology Applications
no code implementations • 8 Dec 2014 • Rikke Ingebrigtsen, Finn Lindgren, Ingelin Steinsland, Sara Martino
Estimation of stationary dependence structure parameters using only a single realisation of the spatial process, typically leads to inaccurate estimates and poorly identified parameters.
Methodology Applications
no code implementations • 25 Jun 2019 • Per Sidén, Finn Lindgren, David Bolin, Anders Eklund, Mattias Villani
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors have been shown to produce state-of-the-art activity maps without pre-smoothing the data.
Methodology Applications Computation
1 code implementation • 9 Jul 2019 • Joaquín Martínez-Minaya, Finn Lindgren, Antonio López-Quílez, Daniel Simpson, David Conesa
This paper introduces a Laplace approximation to Bayesian inference in Dirichlet regression models, which can be used to analyze a set of variables on a simplex exhibiting skewness and heteroscedasticity, without having to transform the data.
Bayesian Inference Computation Methodology
no code implementations • 7 Nov 2023 • Victor Medina-Olivares, Finn Lindgren, Raffaella Calabrese, Jonathan Crook
In credit risk analysis, survival models with fixed and time-varying covariates are widely used to predict a borrower's time-to-event.