@inproceedings {514,
	title = {Recursive multiscale estimation of space-time random fields},
	booktitle = {International Conference on Image Processing},
	year = {1999},
	abstract = {<p>We recently developed a multiscale recursive estimation procedure for the estimation of large-scale dynamic systems. The procedure propagates multiscale models for the estimation errors more efficiently than the Kalman filter\&$\#$39;s propagation of the error covariances, with a resulting computational complexity of Oscr;(N) and Oscr;(N3/2 ), where N is the number of variables estimated, for 1-D and 2-D dynamic systems, respectively. To further reduce the computational cost, we introduce in this paper a new class of reduced-order spatially-interpolated multiscale models, and demonstrate their use in remote</p>
},
	keywords = {computational complexity, estimation errors, image processing, large-scale dynamic systems, multiscale models, recursive estimation, recursive multiscale estimation, reduced-order spatially-interpolated multiscale models, space-time adaptive processing, space-time random fields},
	doi = {http://dx.doi.org/10.1109/ICIP.1999.823021},
	author = {T T. Ho and P Fieguth and A S. Wilsky}
}
@inproceedings {473,
	title = {Computationally efficient multiscale estimation of large-scale dynamic systems},
	booktitle = {International Conference on Image Processing},
	year = {1998},
	abstract = {<p>Statistical estimation of large-scale dynamic systems governed by stochastic partial differential equations is important in a wide range of scientific applications. However, the realization of computationally efficient algorithms for statistical estimation of such dynamic systems is very difficult. Conventional linear least squares methods are impractical for both computational and storage reasons. A previously-developed multiscale estimation methodology has been successfully applied to a number of large-scale static estimation problems. In this paper we apply the multiscale approach to the more challenging dynamic estimation problems, introducing a recursive procedure that efficiently propagates multiscale models for the estimation errors in a manner analogous to, but more efficient than, the Kalman filter\&$\#$39;s propagation of the error covariances. We illustrate our research in the context of 1-D and 2-D diffusive processes</p>
},
	keywords = {1-D diffusive process, 2-D diffusive processes, computationally efficient multiscale estimation, dynamic estimation, estimation errors, iterative methods, large-scale dynamic systems, partial differential equations, recursive estimation, recursive procedure, statistical analysis, statistical estimation, stochastic partial differential equations, stochastic processes},
	doi = {http://dx.doi.org/10.1109/ICIP.1998.999018},
	author = {T T. Ho and P Fieguth and A S. Willsky}
}
