Belief propagation in fuzzy Bayesian networks

Fogelberg, C. and Pallade, V. and Assheton, P. (2008) Belief propagation in fuzzy Bayesian networks. In: 1st International Workshop on Combinations of Intelligent Methods and Applications, CIMA 2008, 22 July 2008, Patras, Greece.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

Fuzzy Bayesian networks are a generalisation of classic Bayesian networks to networks with fuzzy variable state. This paper describes our formalisation and outlines how belief propagation can be conducted. Fuzzy techniques can lead to more robust inference. A key advantage of our formalisation is that it can take advantage of all existing network inference and Bayesian network algorithms. Another key advantage is that we have developed several techniques to control the algorithmic complexity. When these techniques can be applied it means that fuzzy Bayesian networks are only a small linear factor less efficient than classic Bayesian networks. With appropriate pre-processing they may be substantially more efficient.

Keywords:Algorithmic complexity, Belief propagation, Fuzzy bayesian networks, Fuzzy techniques, Generalisation, Network algorithms, Network inference, Robust inference
Subjects:G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:Professional services > The Library
ID Code:28468
Deposited On:23 Aug 2017 11:11

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