Abstract
In this paper, we introduce a method for approximating the solution to inference and optimization tasks in uncertain and deterministic reasoning. Such tasks are in general intractable for exact algorithms because of the large number of dependency relationships in their structure. Our method e.ectively maps such a dense problem to a sparser one which is in some sense “closest”. Exact methods can be run on the sparser problem to derive bounds on the original answer, which can be quite sharp. On one large CPCS network, for example, we were able to calculate upper and lower bounds on the conditional probability of a variable, given evidence, that were almost identical in the average case.