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 ModelsCAR ModelROC AnalysisProcedureBiasVarianceMean 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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