Artificial intelligence and statistics
Artificial intelligence and statistics
A statistical view of uncertainty in expert systems
Artificial intelligence and statistics
Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic inference and influence diagrams
Operations Research
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Probabilistic similarity networks
Probabilistic similarity networks
Uncertainty in Artificial Intelligence
Uncertainty in Artificial Intelligence
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
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Probabilistic networks, used as an adjunct or alternative to the logical models used in artificial intelligence (AI) and decision support systems (DSS), offer a way to compactly represent a distribution over a set of random variables. Nonetheless, the specification of a given network may require conditional probabilities that are simply unavailable. In this paper a means for analyzing incompletely specified networks is presented, and some general rules are derived from the application of the method to some simple networks. The use of the technique in MIS settings is illustrated.