3 papers
stat.ML2020
Deep Gaussian Markov Random Fields
Per Sidén, Fredrik Lindsten
Gaussian Markov random fields (GMRFs) are probabilistic graphical models widely used in spatial statistics and related fields to model dependencies over spatial structures. We esta…
stat.ME2019
Anatomically informed Bayesian spatial priors for fMRI analysis
David Abramian, Per Sidén, Hans Knutsson +2
Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical bounda…
stat.ME2019
Spatial 3D Matérn priors for fast whole-brain fMRI analysis
Per Sidén, Finn Lindgren, David Bolin +2
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors has been shown to produce state-of-the-art activity maps…