| Paper: | SPTM-P10.10 | ||
| Session: | Estimation | ||
| Time: | Thursday, May 18, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, Classification Theory and Applications | ||
| Title: | Low-Rank Variance Estimation in Large-Scale GMRF Models | ||
| Authors: | Dmitry Malioutov, Jason Johnson, Alan Willsky, Massachusetts Institute of Technology, United States | ||
| Abstract: | We consider the problem of variance estimation in large-scale Gauss-Markov random field (GMRF) models. While approximate mean estimates can be obtained efficiently for sparse GMRFs of very large size, computing the variances is a challenging problem. We propose a simple rank-reduced method which exploits the graph structure and the correlation length in the model to compute approximate variances with linear complexity in the number of nodes. The method has a separation length parameter trading off complexity versus estimation accuracy. For models with bounded correlation length, we efficiently compute provably accurate variance estimates. | ||
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