An Efficient Method for Probabilistic Knowledge Integration

Authors: Shenyong Zhang, Yun Peng, and Xiaopu Wang

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

Date: November 03, 2008

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

Publisher: IEEE Computer Society

Tags: uncertainty, bayesian reasoning, bayesian reasoning, ipfp

Google Scholar: k0LEI6EWOrwJ

Number of Google Scholar citations: 2 [show citations]

Number of downloads: 730

Available for download as

size: 177379 bytes

Bookmark at: Digg | Del.icio.us | Connotea | CiteULike