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Restricted Prior Inference for Complex Uncertainty Structures

Restricted Prior Inference for Complex Uncertainty Structures The problem of representing and analysing partial aspects of uncertainty is examined using a geometric approach. A Hilbert space of random objects is constructed, where the inner product captures aspects of beliefs about the relationship between the objects. Orthogonal direct sums of the Hilbert space are used to restrict the amount of detail that is required for the prior specification. Using minimal assumptions of temporal consistency, this geometric space is adapted to derive the stochastic relationships between the formal restricted partial belief analysis and the corresponding posterior uncertainty judgements. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

Restricted Prior Inference for Complex Uncertainty Structures

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References (16)

Publisher
Springer Journals
Copyright
Copyright © 2001 by Kluwer Academic Publishers
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mathematics, general; Computer Science, general; Complex Systems
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1023/A:1016782020717
Publisher site
See Article on Publisher Site

Abstract

The problem of representing and analysing partial aspects of uncertainty is examined using a geometric approach. A Hilbert space of random objects is constructed, where the inner product captures aspects of beliefs about the relationship between the objects. Orthogonal direct sums of the Hilbert space are used to restrict the amount of detail that is required for the prior specification. Using minimal assumptions of temporal consistency, this geometric space is adapted to derive the stochastic relationships between the formal restricted partial belief analysis and the corresponding posterior uncertainty judgements.

Journal

Annals of Mathematics and Artificial IntelligenceSpringer Journals

Published: Oct 19, 2004

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