Logical Compilation of Bayesian Networks with Discrete Variables

This paper presents a new approach to inference in Bayesian networks. The principal idea is to encode the network by logical sentences and to compile the resulting encoding into an appropriate form. From there, all possible queries are answerable in linear time relative to the size of the logical form. Therefore, our approach is a potential solution for real-time applications of probabilistic inference with limited computational resources. The underlying idea is similar to both the differential and the weighted model counting approach to inference in Bayesian networks, but at the core of the proposed encoding we avoid the transformation from discrete to Boolean variables. This alternative encoding enables a more natural solution.

• Type: Conference
• Year: 2007
• Where: ECSQARU
• Authors: Michael Wachter, Rolf Haenni
• Keywords: Bayesian Networks, ECSQARU 2007, Inference, Model Counting Approach