Conference Paper

Conditioning Graphs: Practical Structures for Inference in Bayesian Networks.

Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
DOI: 10.1007/11589990_8 Conference: AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, December 5-9, 2005, Proceedings
Source: DBLP


Programmers employing inference in Bayesian networks typ- ically rely on the inclusion of the model as well as an inference engine into their application. Sophisticated inference engines require non-trivial amounts of space and are also difficult to implement. This limits their use in some applications that would otherwise benefit from probabilistic inference. This paper presents a system that minimizes the space require- ment of the model. The inference engine is sufficiently simpleas to avoid space-limitation and be easily implemented in almost any environment. We show a fast, compact indexing structure that is linear in the size of the network. The additional space required to compute over the model is linear in the number of variables in the network.

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