Making Maximum Entropy Computations Easier ByAdding Extra Constraints
This paper presents a new way to compute the probability distribution with maximum entropy satisfying a set of constraints Unlike previous ap proaches our method is integrated with the planning of data collection and tabulation We show how adding constraints and performing the associated additional tabulations can substantially speed up computation by replacing the usual iterative techniques with a straight forward computation These extra constraints are shown to correspond to the intermediate tables used in Cheeseman s method We also show that the class of constraint graphs that our method handles is a proper generalization of Pearl s singly connected networks An open problem is to determine a minimal set of constraints necessary to make a hypergraph acyclic We conjecture that this problem is NP complete and discuss heuristics to approximate the optimal solution