An Efficient Method for Probabilistic Knowledge Integration

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https://ieeexplore.ieee.org/document/4669772

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10.1109/ICTAI.2008.57
 http://hdl.handle.net/11603/12117

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UMBC Computer Science and Electrical Engineering Department
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2008-11-03

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conference papers and proceedings preprints
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Citation of Original Publication

Shenyong Zhang, Yun Peng, and Xiaopu Wang, An Efficient Method for Probabilistic Knowledge Integration, Proceedings of The 20th IEEE International Conference on Tools with Artificial Intelligence, 2008, https://ieeexplore.ieee.org/document/4669772

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© 2008 IEEE

Subjects

Probabilistic
Knowledge Integration
SMOOTH
Bayesian network
statistical distributions
UMBC Ebiquity Research Group
iterative proportional fitting procedure (IPFP)

Abstract

This paper presents an efficient method, SMOOTH, for modifying a joint probability distribution to satisfy a set of inconsistent constraints. It extends the well-known “iterative proportional fitting procedure” (IPFP), which only works with consistent constraints. Comparing with existing methods, SMOOTH is computationally more efficient and insensitive to data. Moreover, SMOOTH can be easily integrated with Bayesian networks for Bayes reasoning with inconsistent constraints.