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: | Shenoy Prakash P. |
Keywords: | programming: markov decision, programming: dynamic |
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.