A model for compressing probabilities in belief networks

Zhang, Shichao and Zhang, Chengqi 2001, A model for compressing probabilities in belief networks, Informatica, vol. 25, no. 3, pp. 409-419.

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Title A model for compressing probabilities in belief networks
Author(s) Zhang, Shichao
Zhang, Chengqi
Journal name Informatica
Volume number 25
Issue number 3
Start page 409
End page 419
Publisher Slovensko Drustvo Informatika
Place of publication Ljubljana, Slovenia
Publication date 2001
ISSN 0350-5596
Keyword(s) Probabilistic Reasoning
Belief Network
Fuzzy Reasoning
Compressibility of Information
Encode Technology
Summary Probabilistic reasoning with belief (Bayesian) networks is based on conditional probability matrices. Thus it suffers from NP-hard implementations. In particular, the amount of probabilistic information necessary for the computations is often overwhelming. So, compressing the conditional probability table is one of the most important issues faced by the probabilistic reasoning community. Santos suggested an approach (called linear potential functions) for compressing the information from a combinatorial amount to roughly linear in the number of random variable assignments. However, much of the information in Bayesian networks, in which there are no linear potential functions, would be fitted by polynomial approximating functions rather than by reluctantly linear functions. For this reason, we construct a polynomial method to compress the conditional probability table in this paper. We evaluated the proposed technique, and our experimental results demonstrate that the approach is efficient and promising.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30001417

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