Gaussian Multiresolution Models: Exploiting Sparse Markov and Covariance Structure

Gaussian Multiresolution Models: Exploiting Sparse Markov and Covariance Structure

In this paper, we consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale, and the coarser, hidden variables serve to capture long-distance dependencies. Tree-structured MR models have limited modeling capabilities, as variables at o...

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Bibliographic Details
Main Authors: Choi, Myung Jin, Chandrasekaran, Venkat, Willsky, Alan S.
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Article
Language:en_US
Published: Institute of Electrical and Electronics Engineers 2010
Subjects:
multiresolution (MR) models
multipole methods
hidden variables
graphical models
Gauss–Markov random fields
Online Access:http://hdl.handle.net/1721.1/58956
https://orcid.org/0000-0003-0149-5888

Internet

http://hdl.handle.net/1721.1/58956
https://orcid.org/0000-0003-0149-5888

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