Computational aspects in checking of coherence and propagation of conditional probability bounds

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Abstract

In this paper we consider the problem of reducing the computationaldifficulties in g-coherence checking and propagation of imprecise conditionalprobability assessments. We review some theoretical results related with thelinear structure of the random gain in the betting criterion. Then, we proposea modi ed version of two existing algorithms, used for g-coherence checkingand propagation, which are based on linear systems with a reduced number ofunknowns. The reduction in the number of unknowns is obtained by an iterativealgorithm. Finally, to illustrate our procedure we give some applications.
Lingua originaleEnglish
Pagine1-13
Numero di pagine12
Stato di pubblicazionePublished - 2000

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Probability Bounds
Conditional probability
Propagation
Linear Systems
Unknown
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abstract = "In this paper we consider the problem of reducing the computationaldifficulties in g-coherence checking and propagation of imprecise conditionalprobability assessments. We review some theoretical results related with thelinear structure of the random gain in the betting criterion. Then, we proposea modi ed version of two existing algorithms, used for g-coherence checkingand propagation, which are based on linear systems with a reduced number ofunknowns. The reduction in the number of unknowns is obtained by an iterativealgorithm. Finally, to illustrate our procedure we give some applications.",
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AB - In this paper we consider the problem of reducing the computationaldifficulties in g-coherence checking and propagation of imprecise conditionalprobability assessments. We review some theoretical results related with thelinear structure of the random gain in the betting criterion. Then, we proposea modi ed version of two existing algorithms, used for g-coherence checkingand propagation, which are based on linear systems with a reduced number ofunknowns. The reduction in the number of unknowns is obtained by an iterativealgorithm. Finally, to illustrate our procedure we give some applications.

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KW - computation

KW - propagation

KW - random gain

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