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