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Probability theory / Applied mathematics / Networks / Belief propagation / Coding theory / Markov random field / Graph / Mutual information / Graphical models / Graph theory / Mathematics


Quantifying Nonlocal Informativeness in High-Dimensional, Loopy Gaussian Graphical Models Daniel Levine Lab. for Information and Decision Syst. Massachusetts Institute of Technology
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Document Date: 2014-06-12 14:41:36


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San Mateo / Toledo / Bern / New York / /

Company

Ad Hoc Networks / SIAM Journal / Neural Information Processing Systems / Large Linear Systems / BP / MIT Press / /

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Facility

Massachusetts Institute of Technology Cambridge / Massachusetts Institute of Technology / /

IndustryTerm

Linear response algorithms / online-computable performance bounds / message passing algorithm / data processing / scalar networks / iterative algorithm / large networks / belief propagation-like inference algorithm / prior algorithmic technologies / embedded subgraphs algorithm / computing / dense networks / linear systems / Random networks / /

Organization

Jonathan P. How Lab. for Information and Decision Syst / Institute of Technology Cambridge / Gaussian Graphical Models Daniel Levine Lab. for Information and Decision Syst / Massachusetts Institute of Technology / /

Person

Daniel Levine / M. J. Choi / V / Jonathan P. How Lab / Morgan Kaufman / Y. Liu / V / Proc / Jonathan P. How / Ty / John W. Fisher III / /

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ProvinceOrState

New York / California / Massachusetts / /

PublishedMedium

SIAM Journal on Computing / Journal of Machine Learning Research / Journal of Artificial Intelligence Research / /

Technology

proposed algorithm / Linear response algorithms / resulting belief propagation-like inference algorithm / ET algorithm / Extended message passing algorithm / prior algorithmic technologies / iterative algorithm / Machine Learning / ET-based algorithms / embedded subgraphs algorithm / presented algorithm / /

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