Simplifying Probability Elicitation and Uncertainty Modeling in Bayesian Networks
نویسندگان
چکیده
In this paper we contribute two novel methods that simplify the demands of knowledge elicitation for particular types of Bayesian networks. The first method simplifies the task of experts providing conditional probabilities when the states that a random variable takes can be described by a fully ordered set. In this order, each state’s definition is inclusive of the preceding state’s definition. Knowledge for the state is then elicited as a conditional probability of the preceding state. The second method leverages the 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.
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تاریخ انتشار 2011