Search Results for author: Per Sidén

Found 3 papers, 2 papers with code

Scalable Deep Gaussian Markov Random Fields for General Graphs

1 code implementation10 Jun 2022 Joel Oskarsson, Per Sidén, Fredrik Lindsten

We propose a flexible GMRF model for general graphs built on the multi-layer structure of Deep GMRFs, originally proposed for lattice graphs only.

Bayesian Inference Variational Inference

Deep Gaussian Markov Random Fields

1 code implementation ICML 2020 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.

Variational Inference

Spatial 3D Matérn priors for fast whole-brain fMRI analysis

no code implementations25 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

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