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On the complexity of inference about probabilistic relational models
2000
Artificial Intelligence
We investigate the complexity of probabilistic inference from knowledge bases that encode probability distributions on finite domain relational structures. Our interest here lies in the complexity in terms of the domain under consideration in a specific application instance. We obtain the result that assuming NETIME = ETIME this problem is not polynomial for reasonably expressive representation systems. The main consequence of this result is that it is unlikely to find inference techniques with
doi:10.1016/s0004-3702(99)00109-5
fatcat:kjvyvlma4nggtnvhuc4yqp7dp4