Incremental Probabilistic Inference
نویسنده
چکیده
Propositional representation services such as truth maintenance systems offer pow erful support for incremental, interleaved, problem-model construction and evaluation. Probabilistic inference systems, in contrast, have lagged behind in supporting this incre mentality typically demanded by problem solvers. The problem, we argue, is that the basic task of probabilistic inference is typi cally formulated at too large a grain-size. We show how a system built around a smaller grain-size inference task can have the desired incrementality and serve as the basis for a low-level (propositional) probabilistic repre sentation service.
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