An Efficient Method for Probabilistic Knowledge Integration
Links to Fileshttps://ieeexplore.ieee.org/document/4669772
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Type of Work4 pages
conference papers and proceedings preprints
Citation of Original PublicationShenyong 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
UMBC Ebiquity Research Group
iterative proportional fitting procedure (IPFP)
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.