Importance sampling on relational Bayesian networks

We present techniques for importance sampling from distributions defined representation language, and therefore can be applied in situations where sampling from a standard Bayesian Network representation is infeasible. We describe experimental results from using standard, adaptive and backward sampling strategies. Furthermore, we use in our experiments a model that illustrates a fully general way of translating the recent framework of Markov Logic Networks into Relational Bayesian Networks