Fast factorisation of probabilistic potentials and its application to approximate inference in Bayesian networks

摘要

We present an efficient procedure for factorising probabilistic potentials represented asprobability trees. This new procedure is able to detect some regularities that cannot becaptured by existing methods. In cases where an exact decomposition is not achievable,we propose a heuristic way to carry out approximate factorisations guided by a parametercalled factorisation degree, which is fast to compute. We show how this parameter can beused to control the tradeoff between complexity and accuracy in approximate inferencealgorithms for Bayesian networks.