Name
Abstract | ||
|---|---|---|
We present a versatile framework for tractable computation of approximate variances in large-scale Gaussian Markov random field estimation problems. In addition to its efficiency and simplicity, it also provides accuracy guarantees. Our approach relies on the construction of a certain low-rank aliasing matrix with respect to the Markov graph of the model. We first construct this matrix for single-scale models with short-range correlations and then introduce spliced wavelets and propose a construction for the long-range correlation case, and also for multiscale models. We describe the accuracy guarantees that the approach provides and apply the method to a large interpolation problem from oceanography with sparse, irregular, and noisy measurements, and to a gravity inversion problem. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TSP.2008.927482 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
accuracy guarantee,large interpolation problem,random field estimation problem,multiscale model,gravity inversion problem,approximate variance,certain low-rank,long-range correlation case,markov graph,gmrf models,large-scale gaussian markov,multiscale approaches,low-rank variance approximation,markov processes,gaussian processes,approximation theory,gravity,interpolation,graph theory,estimation theory,random processes,sparse matrices,random variables,wavelets,inverse problem,multiscale modeling,wavelet transforms | Mathematical optimization,Markov process,Random field,Markov model,Interpolation,Markov chain,Gaussian process,Estimation theory,Sparse matrix,Mathematics | Journal |
Volume | Issue | ISSN |
56 | 10 | 1053-587X |
Citations | PageRank | References |
12 | 0.75 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dmitry M. Malioutov | 1 | 1052 | 86.85 |
| J.K. Johnson | 2 | 12 | 0.75 |
| Myung Choi | 3 | 16 | 2.21 |
| Alan S. Willsky | 4 | 7466 | 847.01 |