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Imprecise data fusion. (La fusion d’informations imprĂ©cises.) (French) Zbl 0829.60001
Summary: Possibility theory offers a natural setting for representing imprecise data and poor information. This theory turns out to be quite useful for the purpose of pooling pieces of information stemming from several sources (for instance, several experts, sensors, or databases). Indeed it looks more flexible than probability theory for the representation of aggregation modes that do not express averaging processes. This paper tentatively explains why possibility theory is appealing for the fusion of imprecise data, and it describes several aggregation modes it allows, along with their underlying assumptions. The existence of adaptive combination rules are pointed out, that take into account the level of conflict between the sources. This approach sounds natural in the pooling of expert opinions. It is suggested here that, under some assumptions, it might also be useful in sensor data fusion.

MSC:
60A99 Foundations of probability theory
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