Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICASSP.2006.1660744 | ICASSP |
Keywords | Field | DocType |
Gaussian processes,Markov processes,graph theory,Gauss-Markov random field models,correlation length,graph structure,large-scale GMRF models,low-rank variance estimation,rank-reduced method,separation length parameter | Graph theory,Mathematical optimization,Random field,Markov process,Variance estimation,Correlation function (statistical mechanics),Gaussian process,Linear complexity,Mathematics,Bounded function | Conference |
Volume | ISSN | Citations |
3 | 1520-6149 | 8 |
PageRank | References | Authors |
0.93 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dmitry M. Malioutov | 1 | 1052 | 86.85 |
| Jason K. Johnson | 2 | 201 | 14.07 |
| Alan S. Willsky | 3 | 7466 | 847.01 |