Building Probabilistic Networks: Where Do the Numbers Come From? — a Guide to the Literature
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چکیده
Probabilistic networks are now fairly well established as practical representations of knowledge for reasoning under uncertainty, as demonstrated by an increasing number of successful applications in such domains as (medical) diagnosis and prognosis, planning, vision, information retrieval, and natural language processing. A probabilistic network (also referred to as a belief network, Bayesian network, or, somewhat imprecisely, causal network) consists of a graphical structure, encoding a domain’s variables and the qualitative relationships between them, and a quantitative part, encoding probabilities over the variables [Pearl, 1988]. Building a probabilistic network for a domain of application involves three tasks. The first of these is to identify the variables that are of importance, along with their possible values. Once the important domain variables have been identified, the second task is to identify the relationships between the variables discerned and to express these in a graphical structure. The tasks of eliciting the variables and values of importance as well as
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Building Probabilistic Networks: aWhere Do the Numbers Come From?o Guest Editors' Introduction
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Building Probabilistic Networks: "Where Do the Numbers Come From?" Guest Editors Introduction
Probabilistic networks are now fairly well established as practical representations of knowledge for reasoning under uncertainty, as demonstrated by an increasing number of successful applications in such domains as (medical) diagnosis and prognosis, planning, vision, information retrieval, and natural language processing. A probabilistic network (also referred to as a belief network, Bayesian ...
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