92 citations · 498 across the 46 of their papers we have counts for
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Fitting latent non-Gaussian models using variational Bayes and Laplace approximations
Rafael Cabral, David Bolin, Håvard Rue
Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical applications. Nevertheless, in areas ranging from longitudinal studies in biostatist…
Fast Bayesian estimation of brain activation with cortical surface fMRI data using EM
Daniel A. Spencer, David Bolin, Amanda F. Mejia
Task functional magnetic resonance imaging (fMRI) is a type of neuroimaging data used to identify areas of the brain that activate during specific tasks or stimuli. These data are…
Covariance-based rational approximations of fractional SPDEs for computationally efficient Bayesian inference
David Bolin, Alexandre B. Simas, Zhen Xiong
The stochastic partial differential equation (SPDE) approach is widely used for modeling large spatial datasets. It is based on representing a Gaussian random field on $\mathbb…
Gaussian Whittle-Matérn fields on metric graphs
David Bolin, Alexandre B. Simas, Jonas Wallin
We define a new class of Gaussian processes on compact metric graphs such as street or river networks. The proposed models, the Whittle--Matérn fields, are defined via a fractional…
Controlling the flexibility of non-Gaussian processes through shrinkage priors
Rafael Cabral, David Bolin, Håvard Rue
The normal inverse Gaussian (NIG) and generalized asymmetric Laplace (GAL) distributions can be seen as skewed and semi-heavy-tailed extensions of the Gaussian distribution. Models…
Fast Bayesian estimation of brain activation with cortical surface and subcortical fMRI data using EM
Daniel Spencer, David Bolin, Mary Beth Nebel +1
Analysis of brain imaging scans is critical to understanding the way the human brain functions, which can be leveraged to treat injuries and conditions that affect the quality of l…