Sampling from Gaussian graphical models using subgraph perturbations
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Willsky_Sampling from.pdf
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160.85 KB
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0941255fa6e46daf1c5d6b4657c32b05
Author(s)
Liu, Ying
Kosut, Oliver
Willsky, Alan S.
Date Issued
July 2013
Journal
Proceedings of the 2013 IEEE International Symposium on Information Theory
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Liu, Ying, Oliver Kosut, and Alan S. Willsky. “Sampling from Gaussian Graphical Models Using Subgraph Perturbations.” 2013 IEEE International Symposium on Information Theory (July 2013).
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ISIT.2013.6620676
