Anders L. Madsen, Aalborg University and Bruce D'Ambrosio, Oregon State University
The efficiency of algorithms for probabilistic inference in Bayesian networks can be improved by exploiting independence of causal influence. The factorized representation of independence of causal influence offers a factorized decomposition of certain independence of causal influence models. We describe how lazy propagation - a junction tree based inference algorithm - easily can be extended to take advantage of the decomposition offered by the factorized representation. We introduce two extensions to the factorized representation easing the knowledge acquisition task and reducing the space complexity of the representation exponentially in the state space size of the effect variable of an independence of causal influence model. Finally, we describe how the factorized representation can be used to solve tasks such as calculating the maximum a posteriori hypothesis, the maximum expected utility, and the most probable configuration.