Approximate Inference Algorithm Based on Adaptive Ant Colony and Artificial Fish Swarm Algorithm for Credal Networks

Abstract:

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This paper presents a new algorithm for approximate inference in credal networks (that is, models based on directed acyclic graphs and interval-valued probabilities). Approximate inference in credal networks can be considered as multistage decision in this paper. It is looked as combinatorial optimization problems that obtaining the extreme posteriors from the combinations of various vertices in credal networks. Based on this, the paper combines two intelligence swarm algorithms (ant colony algorithm and artificial fish swarm algorithm) to obtain interval posterior probabilities of query variable for the states of given evidence variables.

Info:

Periodical:

Advanced Materials Research (Volumes 219-220)

Edited by:

Helen Zhang, Gang Shen and David Jin

Pages:

1504-1508

DOI:

10.4028/www.scientific.net/AMR.219-220.1504

Citation:

Y. Qu and P. Zhou, "Approximate Inference Algorithm Based on Adaptive Ant Colony and Artificial Fish Swarm Algorithm for Credal Networks", Advanced Materials Research, Vols. 219-220, pp. 1504-1508, 2011

Online since:

March 2011

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$35.00

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