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Applied Intelligence

, Volume 40, Issue 4, pp 575–591 | Cite as

Approximate inference for dynamic Bayesian networks: sliding window approach

  • Xiao-Guang GaoEmail author
  • Jun-Feng Mei
  • Hai-Yang Chen
  • Da-Qing Chen
Article

Abstract

Dynamic Bayesian networks (DBNs) are probabilistic graphical models that have become a ubiquitous tool for compactly describing statistical relationships among a group of stochastic processes. A suite of elaborately designed inference algorithms makes it possible for intelligent systems to use a DBN to make inferences in uncertain conditions. Unfortunately, exact inference or even approximation in a DBN has been proved to be NP-hard and is generally computationally prohibitive. In this paper, we investigate a sliding window framework for approximate inference in DBNs to reduce the computational burden. By introducing a sliding window that moves forward as time progresses, inference at any time is restricted to a quite narrow region of the network. The main contributions to the sliding window framework include an exploration of its foundations, explication of how it operates, and the proposal of two strategies for adaptive window size selection. To make this framework available as an inference engine, the interface algorithm widely used in exact inference is then integrated with the framework for approximate inference in DBNs. After analyzing its computational complexity, further empirical work is presented to demonstrate the validity of the proposed algorithms.

Keywords

Dynamic Bayesian networks Approximate inference Sliding window Interface algorithm 

Notes

Acknowledgements

This work was supported by the National Nature Science Foundation of China (NSFC) under Grant 60774064 and the doctoral Fund of Ministry of Education of China under Grant 20116102110026. The authors thank the anonymous reviews for their insightful comments and constructive suggestions to improve this paper. We would also like to thank Wen Zengkui for his valuable advice on the structure of the paper and his remarkable work of spelling checking.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Xiao-Guang Gao
    • 1
    Email author
  • Jun-Feng Mei
    • 1
  • Hai-Yang Chen
    • 2
  • Da-Qing Chen
    • 3
  1. 1.School of Electronic and InformationNorthwestern Polytechnical UniversityXi’anChina
  2. 2.School of Electronic InformationXi’an Polytechnic UniversityXi’anChina
  3. 3.Dept. of InformaticsLondon South Bank UniversityLondonUK

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