Dynamic importance sampling in Bayesian networks using factorisation of probability trees

Factorisation of probability trees is a useful tool for inference in Bayesian networks. Probabilistic potentials some of whose parts are proportional can be decomposed as a product of smaller trees. Some algorithms, like lazy propagation, can take advantage of this fact. Also, the factorisation can be used as a tool for approximating inference, if the decomposition is carried out even if the proportionality is not completely reached. In this paper we propose the use of approximate factorisation as a means of controlling the approximation level in a dynamic importance sampling algorithm.