Functional magnetic resonance imaging (fMRI) has recently become a popular tool for studying human brain activity. Despite its widespread use, most existing statistical methods for analyzing fMRI data are problematic. Many methodologies oversimplify the problem for the sake of computational efficiency, often not providing a full statistical model as a result. Other methods are too computationally inefficient to use on large data sets. In this paper, we propose a Bayesian method for analyzing fMRI data that is computationally efficient and provides a full statistical model.
University of Minnesota Ph.D. dissertation. January 2015. Major: Statistics. Advisors: Galin Jones and
John Hughes. 1 computer file (PDF); xii, 124 pages.
Bezener, Martin Andrew.
Bayesian spatiotemporal modeling using spatial hierarchical priors with applications to functional magnetic resonance imaging.
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