Titlebook: Uncertainty in Knowledge Bases; 3rd International Co Bernadette Bouchon-Meunier,Ronald R. Yager,Lotfi A Conference proceedings 1991 Springe

elastic

殺蟲劑

聚集

小母馬

A study of probabilities and belief functions under conflicting evidence: Comparisons and new method conditional probabilities. Several results are obtained showing if and when the two methodologies produce the same results. The role played by the normalizing constant is shown to be tied to prior probability of the hypothesis if equality is to occur. This forces further relationships between the

fetter

Propagating belief functions through constraints systems,andom or uncertain sets, uncertainty in such models is quite naturally and more generally described by belief functions. Here a special class of constraint systems induced by the additive underlying group structure is considered. Belief functions are used to specify uncertain constraints on relation

aneurysm

Seizure

AMOR

Probabilistic default reasoning, levels the statements of the lower levels are ignored. The approach is applicable to inference networks of arbitrary structure including loops and cycles. The simulated annealing algorithm may be used to derive a distribution which best fits to the different statements according to the maximum like

Veneer

On knowledge representation in belief networks,le form, and (iii) using the information to draw inferences about specific problem instances. In the artificial intelligence (AI) literature, the first element is referred to as knowledge acquisition, while the second and third are embodied in a system‘s knowledge base and inference engine, respecti

過時(shí)

,Stoss — A stochastic simulation system for Bayesian belief networks,tic reasoning for Bayesian belief networks. The system is then applied to an artificial example in the field of forensic science and the results are compared with the calculations obtained using the Causal Probabilistic Reasoning System (CPRS).