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Many applications of the Fellegi-Sunter model use simplifying assumptions and ad hoc modifications
to improve matching efficacy. Because of model misspecification, distinctive approaches developed
in one application typically cannot be used in other applications and do not always make use of
advances in statistical and computational theory. An Expectation-Maximization (EMH) algorithm that
constrains the estimates to a convex subregion of the parameter space is given. The EMH algorithm
provides probability estimates that yield better decision rules than unconstrained estimates. The
algorithm is related to results of Meng and Rubin (1993) on Multi-Cycle Expectation-Conditional
Maximization algorithms and make use of results of Haberman (1977) that hold for large classes of