Valuation-based systems for Bayesian decision analysis

Valuation-based systems for Bayesian decision analysis

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Article ID: iaor1993280
Country: United States
Volume: 40
Issue: 3
Start Page Number: 463
End Page Number: 484
Publication Date: May 1992
Journal: Operations Research
Authors:
Keywords: programming: markov decision, programming: dynamic
Abstract:

This paper proposes a new method for representing and solving Bayesian decision problems. The representation is called a valuation-based system and has some similarities to influence diagrams. However, unlike influence diagrams which emphasize conditional independence among random variables, valuation-based systems emphasize factorizations of joint probability distributions. Also, whereas influence diagram representation allows only conditional probabilities, valuation-based system representation allows all probabilities. The solution method is a hybrid of local computational methods for the computation of marginals of joint probability distributions and the local computational methods for discrete optimization problems. The present representation and solution methods are briefly compared to those of influence diagrams.

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