An Efficient Method for Probabilistic Knowledge Integration

Authors: , Yun Peng, Xiaopu Wang

Book Title: Proceedings of The 20th IEEE International Conference on Tools with Artificial Intelligence

Date:

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.

Type: InProceedings

Tags: bayesian reasoning, ipfp, uncertainty

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