Encoding Probability Propagation in Belief Networks

Publisher:
IEEE Press
Publication Type:
Journal Article
Citation:
IEEE Transactions on Systems, Man and Cybernetics (Part A), 2002, 32 (4), pp. 526 - 531
Issue Date:
2002-01
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Complexity reduction is an important task in Bayesian networks. Recently, an approach known as the linear potential function (LPF) model has been proposed for approximating Bayesian computations. The LPF model can effectively compress a conditional probability table into a linear function. This correspondence extends the LPF model to approximate propagation in Bayesian networks. The extension focuses on encoding probability propagation as a polynomial function for a class of tractable problems.
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