Penny, WD; Kilner, J; Blankenburg, F; (2007) Robust Bayesian general linear models. NEUROIMAGE , 36 (3) 661 - 671. 10.1016/j.neuroimage.2007.01.058.
Full text not available from this repository.
We describe a Bayesian learning algorithm for Robust General Linear Models (RGLMs). The noise is modeled as a Mixture of Gaussians rather than the usual single Gaussian. This allows different data points to be associated with different noise levels and effectively provides a robust estimation of regression coefficients. A variational inference framework is used to prevent overtitting and provides a model order selection criterion for noise model order. This allows the RGLM to default to the usual GLM when robustness is not required. The method is compared to other robust regression methods and applied to synthetic data and fMRI. (c) 2007 Elsevier Inc. All rights reserved.
|Title:||Robust Bayesian general linear models|
|Keywords:||Bayesian, fMRI, artefact, mixture model, robust estimation, FMRI TIME-SERIES|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Psychology and Language Sciences (Division of) > Institute of Cognitive Neuroscience|
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology > Imaging Neuroscience
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology > Motor Neuroscience and Movement Disorders
UCL > School of Life and Medical Sciences > Faculty of Life Sciences
Archive Staff Only: edit this record