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Statistical dependencies observed in real-world phenomena often change drastically with time. Graphical dependency models, such as the Markov networks (MNs), must deal with this temporal heterogeneity in order to draw meaningful conclusions about the transient nature of the target phenomena. However, in practice, the estimation of time-varying dependency graphs can be inefficient due to the potentially...
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