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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...
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