92 citations · 498 across the 46 of their papers we have counts for
6 papers · 1 filter
Efficient Covariance Approximations for Large Sparse Precision Matrices
Per Sidén, Finn Lindgren, David Bolin +1
The use of sparse precision (inverse covariance) matrices has become popular because they allow for efficient algorithms for joint inference in high-dimensional models. Many applic…
Calculating probabilistic excursion sets and related quantities using excursions
David Bolin, Finn Lindgren
The R software package excursions contains methods for calculating probabilistic excursion sets, contour credible regions, and simultaneous confidence bands for latent Gaussian sto…
Fast Bayesian whole-brain fMRI analysis with spatial 3D priors
Per Sidén, Anders Eklund, David Bolin +1
Spatial whole-brain Bayesian modeling of task-related functional magnetic resonance imaging (fMRI) is a great computational challenge. Most of the currently proposed methods theref…
Spatially adaptive covariance tapering
David Bolin, Jonas Wallin
Covariance tapering is a popular approach for reducing the computational cost of spatial prediction and parameter estimation for Gaussian process models. However, tapering can have…
Efficient adaptive MCMC through precision estimation
Jonas Wallin, David Bolin
A novel adaptive Markov chain Monte Carlo algorithm is presented. The algorithm utilizes sparsity in the partial correlation structure of a density to efficiently estimate the cova…
How do Markov approximations compare with other methods for large spatial data sets?
David Bolin, Finn Lindgren
The Matérn covariance function is a popular choice for modeling dependence in spatial environmental data. Standard Matérn covariance models are, however, often computationally infe…