Multiply-rooted multiscale models for large-scale estimation

Divide-and-conquer or multiscale techniques have become popular for solving large statistical estimation problems. The methods rely on defining a state which conditionally decorrelates the large problem into multiple subproblems, each more straightforward than the original. However this step cannot...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 10(2001), 11 vom: 15., Seite 1676-86
1. Verfasser: Fieguth, P W (VerfasserIn)
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2001
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article
Beschreibung
Zusammenfassung:Divide-and-conquer or multiscale techniques have become popular for solving large statistical estimation problems. The methods rely on defining a state which conditionally decorrelates the large problem into multiple subproblems, each more straightforward than the original. However this step cannot be carried out for asymptotically large problems since the dimension of the state grows without bound, leading to problems of computational complexity and numerical stability. In this paper, we propose a new approach to hierarchical estimation in which the conditional decorrelation of arbitrarily large regions is avoided, and the problem is instead addressed piece-by-piece. The approach possesses promising attributes: it is not a local method-the estimate at every point is based on all measurements; it is numerically stable for problems of arbitrary size; and the approach retains the benefits of the multiscale framework on which it is based: a broad class of statistical models, a stochastic realization theory, an algorithm to calculate statistical likelihoods, and the ability to fuse local and nonlocal measurements
Beschreibung:Date Completed 25.06.2010
Date Revised 07.02.2008
published: Print
Citation Status PubMed-not-MEDLINE
ISSN:1057-7149
DOI:10.1109/83.967396