Simplifying Probability Elicitation and Uncertainty Modeling in Bayesian Networks
Conference
·
OSTI ID:1024542
In this paper we contribute two methods that simplify the demands of knowledge elicitation for particular types of Bayesian networks. The first method simplify the task of providing probabilities when the states that a random variable takes can be described by a new, fully ordered state set in which a state implies all the preceding states. The second method leverages Dempster-Shafer theory of evidence to provide a way for the expert to express the degree of ignorance that they feel about the estimates being provided.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1024542
- Report Number(s):
- PNNL-SA-77781; TRN: US201119%%454
- Resource Relation:
- Conference: Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference (MAICS 2011), April 16-17, 2011, Cincinnati, OH, 114-119
- Country of Publication:
- United States
- Language:
- English
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