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Volume 4 Number 4 >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/1603

Title: A Bayesian Spatial Mixture Model for FMRI Analysis
Authors: Geliazkova, Maya
Keywords: Spatial Mixture Models
CAR Model
ROC Analysis
Procedure
Bias
Variance
Mean Squared Error
Issue Date: 2010
Publisher: Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Citation: Serdica Journal of Computing, Vol. 4, No 4, (2010), 417p-434p
Abstract: We develop, implement and study a new Bayesian spatial mixture model (BSMM). The proposed BSMM allows for spatial structure in the binary activation indicators through a latent thresholded Gaussian Markov random field. We develop a Gibbs (MCMC) sampler to perform posterior inference on the model parameters, which then allows us to assess the posterior probabilities of activation for each voxel. One purpose of this article is to compare the HJ model and the BSMM in terms of receiver operating characteristics (ROC) curves. Also we consider the accuracy of the spatial mixture model and the BSMM for estimation of the size of the activation region in terms of bias, variance and mean squared error. We perform a simulation study to examine the aforementioned characteristics under a variety of configurations of spatial mixture model and BSMM both as the size of the region changes and as the magnitude of activation changes.
URI: http://hdl.handle.net/10525/1603
ISSN: 1312-6555
Appears in Collections:Volume 4 Number 4

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