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01674 am a22002293u 4500 |
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|a Liu, Ying
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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|a Liu, Ying
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|a Willsky, Alan S.
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|a Kosut, Oliver
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|a Willsky, Alan S.
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|a Sampling from Gaussian graphical models using subgraph perturbations
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|b Institute of Electrical and Electronics Engineers (IEEE),
|c 2014-10-21T17:02:47Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/91051
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|a The problem of efficiently drawing samples from a Gaussian graphical model or Gaussian Markov random field is studied. We introduce the subgraph perturbation sampling algorithm, which makes use of any pre-existing tractable inference algorithm for a subgraph by perturbing this algorithm so as to yield asymptotically exact samples for the intended distribution. The subgraph can have any structure for which efficient inference algorithms exist: for example, tree-structured, low tree-width, or having a small feedback vertex set. The experimental results demonstrate that this subgraph perturbation algorithm efficiently yields accurate samples for many graph topologies.
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|a United States. Air Force Office of Scientific Research (Grant FA9550-12-1-0287)
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|a en_US
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|a Article
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|t Proceedings of the 2013 IEEE International Symposium on Information Theory
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