A review of representation issues and modeling challenges with influence diagrams
Since their introduction in the mid 1970s, influence diagrams have become a de facto standard for representing Bayesian decision problems. The need to represent complex problems has led to extensions of the influence diagram methodology designed to increase the ability to represent complex problems. In this paper, we review the representation issues and modeling challenges associated with influence diagrams. In particular, we look at the representation of asymmetric decision problems including conditional distribution trees, sequential decision diagrams, and sequential valuation networks. We also examine the issue of representing the sequence of decision and chance variables, and how it is done in unconstrained influence diagrams, sequential valuation networks, and sequential influence diagrams. We also discuss the use of continuous chance and decision variables, including continuous conditionally deterministic variables. Finally, we discuss some of the modeling challenges faced in representing decision problems in practice and some software that is currently available.
Year of publication: |
2011
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Authors: | Bielza, Concha ; Gómez, Manuel ; Shenoy, Prakash P. |
Published in: |
Omega. - Elsevier, ISSN 0305-0483. - Vol. 39.2011, 3, p. 227-241
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Publisher: |
Elsevier |
Keywords: | Decision-making under uncertainty Influence diagrams Probabilistic graphical models Sequential decision diagrams Unconstrained influence diagrams Sequential valuation networks Sequential influence diagrams Partial influence diagrams Limited memory influence diagrams Gaussian influence diagrams Mixture of Gaussians influence diagrams Mixture of truncated exponentials influence diagrams Mixture of polynomials influence diagrams |
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