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: | Levine, Daniel (Contributor), How, Jonathan P. (Contributor) |
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Other Authors: | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor) |
Format: | Article |
Language: | English |
Published: |
Association of Uncertainty in Artifical Intelligence,
2015-05-11T18:57:55Z.
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Subjects: | |
Online Access: | Get fulltext |
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