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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers
2010
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/58956 https://orcid.org/0000-0003-0149-5888 |