DocumentCode :
1803397
Title :
An introduction to Bayesian networks in systems and control
Author :
Ashcroft, Michael
Author_Institution :
Comput. Sci. Dept., Uppsala Univ., Uppsala, Sweden
fYear :
2012
fDate :
7-8 Sept. 2012
Firstpage :
1
Lastpage :
6
Abstract :
Bayesian networks are a popular and powerful tool in artificial intelligence. They have a natural application in soft-sensing and filtering for system control. This paper provides an overview of the techniques involved. It proceeds by giving a mathematical overview of what Bayesian networks are and the flavors they come in. It then looks at how they can be created or learnt from data and the situations that lead to the use of ensemble models. Then it examines how they can be used for system analysis, inference and automated decision making. Finally, we look at their use in soft-sensing and dynamic system modeling.
Keywords :
belief networks; filtering theory; systems analysis; Bayesian Networks; artificial intelligence; automated decision making; dynamic system modeling; filtering techniques; inference mechanism; soft-sensing techniques; system analysis; system control; Bayesian methods; Inference algorithms; Joints; Markov processes; Probability distribution; Random variables; Topology; Bayesian networks; decision automation; soft-sensing; stochastic modeling; system control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automation and Computing (ICAC), 2012 18th International Conference on
Conference_Location :
Loughborough
Print_ISBN :
978-1-4673-1722-1
Type :
conf
Filename :
6330539
Link To Document :
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