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Found 4 papers, 2 papers with code
Efficient methods for Gaussian Markov random fields under sparse linear constraints
Methods for inference and simulation of linearly constrained Gaussian Markov Random Fields (GMRF) are computationally prohibitive when the number of constraints is large.
Spatial 3D Matérn priors for fast whole-brain fMRI analysis
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
Linear Mixed-Effects Models for Non-Gaussian Repeated Measurement Data
We consider the analysis of continuous repeated measurement outcomes that are collected through time, also known as longitudinal data.
Methodology
Whole-brain substitute CT generation using Markov random field mixture models
The amount of data needed to train a model for s-CT generation is of the order of 100 million voxels.
