Learning Dynamic Conditional Gaussian Graphical Models

IEEE Transactions on Knowledge and Data Engineering, pp. 703-716, 2018.

Cited by: 7|Bibtex|Views5|DOI:https://doi.org/10.1109/TKDE.2017.2777462
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Other Links: dblp.uni-trier.de|academic.microsoft.com

Abstract:

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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