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