UMBC ebiquity
SMOOTH: an efficient method for probabilistic knowledge integration

SMOOTH: an efficient method for probabilistic knowledge integration

Tim Finin, 3:25pm 12 October 2008

In this week’s ebiquity meeting (10:30am Tue Oct 14), PhD student Shenyong Zhang will present his recent work with Yun Peng on SMOOTY, a new efficient method 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. Compared to existing methods, SMOOTH is computationally more efficient and insensitive to data. Moreover, SMOOTH can be easily integrated with Bayesian networks for Bayesian reasoning with inconsistent constraints. A paper on this work, An Efficient Method for Probabilistic Knowledge Integration will apear in the proceedings of The 20th IEEE International Conference on Tools with Artificial Intelligence next month.


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