Probabilistic decision graphs for optimization under uncertainty |
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Authors: | Finn V Jensen Thomas Dyhre Nielsen |
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Institution: | (1) Medical Imaging Informatics, UCLA David Geffen School of Medicine, Los Angeles, CA, USA;(2) Medical Imaging Informatics Group Department of Radiological Sciences, David Geffen School of Medicine University of California, Los Angeles, 924 Westwood Blvd., Suite 420, Los Angeles, CA 90024, USA;; |
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Abstract: | This paper provides a survey on probabilistic decision graphs for modeling and solving decision problems under uncertainty.
We give an introduction to influence diagrams, which is a popular framework for representing and solving sequential decision
problems with a single decision maker. As the methods for solving influence diagrams can scale rather badly in the length
of the decision sequence, we present a couple of approaches for calculating approximate solutions. The modeling scope of the
influence diagram is limited to so-called symmetric decision problems. This limitation has motivated the development of alternative
representation languages, which enlarge the class of decision problems that can be modeled efficiently. We present some of
these alternative frameworks and demonstrate their expressibility using several examples. Finally, we provide a list of software
systems that implement the frameworks described in the paper. |
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