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An important problem in computational neuro-science is to design algorithms that can capture robustly abrupt changes in the conditional intensity function (CIF) of a stochastic point process model of neural spiking data. Towards this end, we advocate the use of a point process analogue of the total variation denoising algorithm, which trades off the point process likelihood with a total variation...
In this article we study the sparse signal recovery problem in a Bayesian framework using a novel Bootstrapped Sparse Bayesian Learning method. Sparse Bayesian Learning (SBL) framework is an effective tool for pruning out the irrelevant features and ending up with a sparse representation. In SBL the choice of prior over the variances of the Gaussian Scale mixture has been an interesting area of research...
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