Spatial Bayesian variable selection with application to functional magnetic resonance imaging
M Smith, L Fahrmeir - Journal of the American Statistical …, 2007 - Taylor & Francis
We propose a procedure to undertake Bayesian variable selection and model averaging for
a series of regressions located on a lattice. For those regressors that are in common in the …
a series of regressions located on a lattice. For those regressors that are in common in the …
Spatially adaptive mixture modeling for analysis of fMRI time series
Within-subject analysis in fMRI essentially addresses two problems, the detection of brain
regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni …
regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni …
Joint inversion of marine seismic AVA and CSEM data using statistical rock-physics models and Markov random fields
J Chen, GM Hoversten - Geophysics, 2012 - library.seg.org
Joint inversion of seismic AVA and CSEM data requires rock-physics relationships to link
seismic attributes to electric properties. Ideally, we can connect them through reservoir …
seismic attributes to electric properties. Ideally, we can connect them through reservoir …
A spatial capture‐recapture model for territorial species
Advances in field techniques have lead to an increase in spatially referenced capture–
recapture data to estimate a species' population size as well as other demographic …
recapture data to estimate a species' population size as well as other demographic …
Adaptive Bayesian radio tomography
D Lee, D Berberidis… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Radio tomographic imaging (RTI) is an emerging technology to locate physical objects in a
geographical area covered by wireless networks. From the attenuation measurements …
geographical area covered by wireless networks. From the attenuation measurements …
Bayesian spatiotemporal modeling using hierarchical spatial priors, with applications to functional magnetic resonance imaging (with discussion)
Bayesian Spatiotemporal Modeling Using Hierarchical Spatial Priors, with Applications to
Functional Magnetic Resonance Imaging ( Page 1 Bayesian Analysis (2018) 13, Number 4 …
Functional Magnetic Resonance Imaging ( Page 1 Bayesian Analysis (2018) 13, Number 4 …
MRI tissue classification using high-resolution Bayesian hidden Markov normal mixture models
D Feng, L Tierney, V Magnotta - Journal of the American Statistical …, 2012 - Taylor & Francis
Magnetic resonance imaging (MRI) is used to identify the major tissues within a subject's
brain. Classification is usually based on a single image providing one measurement for …
brain. Classification is usually based on a single image providing one measurement for …
Network-based genomic discovery: application and comparison of Markov random-field models
As biological knowledge accumulates rapidly, gene networks encoding genomewide gene–
gene interactions have been constructed. As an improvement over the standard mixture …
gene interactions have been constructed. As an improvement over the standard mixture …
Classification of brain activation via spatial Bayesian variable selection in fMRI regression
S Kalus, PG Sämann, L Fahrmeir - Advances in Data Analysis and …, 2014 - Springer
Functional magnetic resonance imaging (fMRI) is the most popular technique in human
brain mapping, with statistical parametric mapping (SPM) as a classical benchmark tool for …
brain mapping, with statistical parametric mapping (SPM) as a classical benchmark tool for …
Min-max extrapolation scheme for fast estimation of 3D Potts field partition functions. Application to the joint detection-estimation of brain activity in fMRI
In this paper, we propose a fast numerical scheme to estimate Partition Functions (PF) of
symmetric Potts fields. Our strategy is first validated on 2D two-color Potts fields and then on …
symmetric Potts fields. Our strategy is first validated on 2D two-color Potts fields and then on …