Quantifying Nonlocal Informativeness in High-Dimensional, Loopy Gaussian Graphical Models
We consider the problem of selecting informative observations in Gaussian graphical models containing both cycles and nuisances. More specifically, we consider the subproblem of quantifying conditional mutual information measures that are nonlocal on such graphs. The ability to efficiently quantify...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association of Uncertainty in Artifical Intelligence
2015
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| Online Access: | http://hdl.handle.net/1721.1/96957 https://orcid.org/0000-0001-8576-1930 |