Learning Dynamic Conditional Gaussian Graphical Models
IEEE Transactions on Knowledge and Data Engineering, pp. 703-716, 2018.
In the paper, we propose a class of dynamic conditional Gaussian graphical models (DCGGMs) based on a set of nonidentical distribution observations, which changes smoothly with time or condition. Specifically, the DCGGMs model the dynamic output network influenced by conditioning input variables, which are encoded by a set of varying para...More
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