Probabilistic Constraint Satisfaction with Non-Gaussian Noise

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Citation: Russ B. Altman and Cheng Che Chen and William B. Poland and Jaswinder Pal Singh. (1995) Probabilistic Constraint Satisfaction with Non-Gaussian Noise. In KSL-95-68, September,1995.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Russ B. Altman and Cheng Che Chen and William B. Poland and Jaswinder Pal Singh
title Probabilistic Constraint Satisfaction with Non-Gaussian Noise
number KSL-95-68
institution Knowledge Systems, AI Laboratory
address Stanford, CA, USA
year 1995
month September
Bibtex more
note Medical Computer Science
Access Paper
abstract We have previously reported a Bayesian algorithm for determining the coordinates of points in three-dimensional space from uncertain constraints.This method is useful in the determination of biological molecular structure.It is limited, however, by the requirement that the uncertainty in the constraints be normally distributed. In this paper, we present an extension of the original algorithm that allows constraint uncertainty to be represented as a mixture of Gaussians, and thereby allows arbitrary constraint distributions. We illustrate the performance of this algorithm on a problem drawn from the domain of molecular structure determination, in which a multicomponent constraint representation produces a much more accurate solution than the old single component mechanism. The new mechanism uses mixture distributions to decompose the problem into a set of independent problems with unimodal constraint uncertainty. The results of the unimodal subproblems are periodically recombined using Bayes' law, to avoid combinatorial explosion. The new algorithm is particularly suited for parallel implementation.

KSL Technical Report ID: KSL-95-68
Facts about Probabilistic Constraint Satisfaction with Non-Gaussian NoiseRDF feed
Abstract We have previously reported a Bayesian alg … We have previously reported a Bayesian algorithm for determining the coordinates of points in three-dimensional space from uncertain constraints.This method is useful in the determination of biological molecular structure.It is limited, however, by the requirement that the uncertainty in the constraints be normally distributed. In this paper, we present an extension of the original algorithm that allows constraint uncertainty to be represented as a mixture of Gaussians, and thereby allows arbitrary constraint distributions. We illustrate the performance of this algorithm on a problem drawn from the domain of molecular structure determination, in which a multicomponent constraint representation produces a much more accurate solution than the old single component mechanism. The new mechanism uses mixture distributions to decompose the problem into a set of independent problems with unimodal constraint uncertainty. The results of the unimodal subproblems are periodically recombined using Bayes' law, to avoid combinatorial explosion. The new algorithm is particularly suited for parallel implementation. ularly suited for parallel implementation.
Address Stanford, CA, USA  +
Author Russ B. Altman and Cheng Che Chen and William B. Poland and Jaswinder Pal Singh  +
Bibtype techreport  +
Has author Russ B. Altman and Cheng Che Chen and William B. Poland and Jaswinder Pal Singh  +
Has identifier KSL-95-68  +
Has publishing details September,1995  +
Has title Probabilistic Constraint Satisfaction with Non-Gaussian Noise  +
Has where published KSL-95-68  +
Has year 1995  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-95-68  +
Month September  +
Note Medical Computer Science
Number KSL-95-68  +
Process note NO  +
Title Probabilistic Constraint Satisfaction with Non-Gaussian Noise  +
Year 1995  +
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