Estimation and Marginalization Using the Kikuchi Approximation Methods
Neural Computation
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In this paper, we develop a measure-theoretic version of the junction tree algorithm to compute desired marginals of a product function. We reformulate the problem in a measure-theoretic framework, where the desired marginals are viewed as corresponding conditional expectations of a product of random variables. We generalize the notions of independence and junction trees to collections of σ-fields on a space with a signed measure. We provide an algorithm to find such a junction tree when one exists. We also give a general procedure to augment the σ-fields to create independencies, which we call "lifting." This procedure is the counterpart of the moralization and triangulation procedure in the conventional generalized distributive law (GDL) framework, in order to guarantee the existence of a junction tree. Our procedure includes the conventional GDL procedure as a special case. However, it can take advantage of structures at the atomic level of the sample space to produce junction tree-based algorithms for computing the desired marginals that are less complex than those GDL can discover, as we argue through examples. Our formalism gives a new way by which one can hope to find low-complexity algorithms for marginalization problems.