Abstract:
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Abstract Driven by applications from numerous areas such as Social Media, Genomics, Brain Imaging, Macroeconomics etc, sparse Graphical Models have undergone intense inferential development in the last decade or so, with e.g. numerous sparsity inducing algorithms having been developed. However almost all this literature assumes the data at each node consists of a scalar time series. But in practice the nodal data is almost always a vector time series. Here we discuss the extension of some sparsity algorithms to this vector scenario.
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