Computing Marginals with Hierarchical Acyclic Hypergraphs

S. K. M. Wong and T. Lin

How to compute marginals efficiently is one of major concerned problems in probabilistic reasoning systems. Traditional graphical models do not preserve all conditional independencies while computing the marginals. That is, the Bayesian DAGs have to be transformed into a secondary computational structure, normally, acyclic hypergraphs, in order to compute marginals. It is well-known that some conditional independencies will be lost in such a transformation. In this paper, we suggest a new graphical model which not only equivalents to a Bayesian DAG, but also takes advantages of all conditional independencies to compute marginals. The input to our model is a set of conditional probability tables as in the traditional approach.


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