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Dawid, Kjaerulff and Lauritzen (1994) provided a preliminary description of ahybrid between Monte-Carlo sampling methods and exact local computations injunction trees. Utilizing the strengths of both methods, such hybrid inferencemethods has the potential of expanding the class of problems which can besolved under bounded resources as well as solving problems which otherwiseresist exact solutions. The paper provides a detailed description of aparticular instance of such a hybrid scheme; namely, combination of exactinference and Gibbs sampling in discrete Bayesian networks. We argue that thiscombination calls for an extension of the usual message passing scheme ofordinary junction trees.

HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees