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Proceedings Paper

Probabilistic constraint network representation of biological structure
Author(s): Russ B. Altman
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Paper Abstract

A constraint satisfaction paradigm is useful for modeling uncertain biological structure. Under this paradigm, we begin with a general model of a biological structure with a set of structural parameters and their uncertainty. Any new information about the structure is considered a constraint on the values of these parameters. The goal is to combine the initial model with the constraints to find a solution that is compatible with both. In this paper, we describe the basic notions of a constraint satisfaction problem and describe a method for representing biological structure that is based on the principles of Bayesian probability, and formulated as a constraint satisfaction problem. Biological structures are modeled using parameters that are assumed to be normally distributed, with a mean and a variance. We illustrate the application of this method to two different types of biological structural calculations: one in which there is a weak prior model and a large amount of data, and one in which there is a strong prior model and a relatively small amount of data. In each case, the method performs well, and produces not only good estimates of mean structure, but also a useful representation of the uncertainty in the estimate.

Paper Details

Date Published: 8 July 1994
PDF: 11 pages
Proc. SPIE 2299, Mathematical Methods in Medical Imaging III, (8 July 1994); doi: 10.1117/12.179252
Show Author Affiliations
Russ B. Altman, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 2299:
Mathematical Methods in Medical Imaging III
Fred L. Bookstein; James S. Duncan; Nicholas Lange; David C. Wilson, Editor(s)

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