Nested Junction Trees
This work addresses computational bottlenecks in probabilistic inference for researchers and practitioners using Bayesian networks, presenting an incremental improvement over existing methods.
The paper tackles the problem of improving inference efficiency in Bayesian networks by exploiting nested junction trees to reduce space and time costs, achieving reductions as demonstrated through empirical evaluation on ten large real-world networks.
The efficiency of inference in both the Hugin and, most notably, the Shafer-Shenoy architectures can be improved by exploiting the independence relations induced by the incoming messages of a clique. That is, the message to be sent from a clique can be computed via a factorization of the clique potential in the form of a junction tree. In this paper we show that by exploiting such nested junction trees in the computation of messages both space and time costs of the conventional propagation methods may be reduced. The paper presents a structured way of exploiting the nested junction trees technique to achieve such reductions. The usefulness of the method is emphasized through a thorough empirical evaluation involving ten large real-world Bayesian networks and the Hugin inference algorithm.