An Adaptive Bayesian Network Inference Algorithm for Network Situation Awareness

DOI

10.2991/icaise.2013.41How to use a DOI?

Keywords

Cognitive network, bayesian network, inference, adaptive

Abstract

The traditional Bayesian network is relatively fixed, the set of nodes, and intensity dependence relationships are rarely change, thus, it is unable to reflect changes in the actual network state. Such an inaccurate network model is also difficult to inference subsequent network. In order to solve the problem of inference is not accurate enough in traditional model, in this paper, the Markov changes of node parameters with time based on Bayesian network is studied. Next, an adaptive inference sampling strategy is put forward and an adaptive inference model based on Bayesian network is designed, then proposes an adaptive Bayesian network Inference algorithm. Finally, simulation results show the effectiveness of the proposed algorithm compared with other algorithms.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jie Li
AU  - Lingwei Chu
AU  - Cheng Dong
AU  - Xiaoyuan Lu
PY  - 2013/08
DA  - 2013/08
TI  - An Adaptive Bayesian Network Inference Algorithm for Network Situation Awareness
BT  - Proceedings of the 2013 The International Conference on Artificial Intelligence and Software Engineering (ICAISE 2013)
PB  - Atlantis Press
SP  - 194
EP  - 198
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaise.2013.41
DO  - 10.2991/icaise.2013.41
ID  - Li2013/08
ER  -