A Bayesian network approach to explaining time series with changing structure

Intelligent Data Analysis

Volumn 8, Issue 5, 2004, Pages 469-480

  Tucker, Allan ^a   Liu, Xiaohui ^a  

^a BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; REFINING; TIME SERIES;

CHANGING STRUCTURES; DEPENDENCY STRUCTURES; DYNAMIC BAYESIAN NETWORKS; LEARNING STRUCTURE; MULTIVARIATE TIME SERIES; OIL REFINERIES; SEARCH TECHNIQUE; SYNTHETIC-TIME;

LEARNING SYSTEMS;

EID: 38049012105     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2004-8504     Document Type: Article

References (15)

1. J.A. Bilnes, Dynamic Bayesian Multinets, Proceedings of the 16th Annual Conference on Uncertainty in AI, 2000, pp. 38-45.
2. C. Chatfield, The Analysis of Time Series-An Introduction, Chapman and Hall, 4th edition, 1989.
3. G.F. Cooper and E. Herskovitz, A Bayesian Method for the Induction of Probabilistic Networks from Data, Machine Learning 9 (1992), 309-347.
4. N. Friedman, Learning the Structure of Dynamic Probabilistic Networks, Proceedings of the 14th Annual Conference on Uncertainty in AI, 1998, pp. 139-147.
5. N. Friedman, The Bayesian Structural EM Algorithm, Proceedings of the 14th Annual Conference on Uncertainty in AI, 1998, pp. 129-138.
6. Z. Ghahramani and G.E. Hinton, Variational Learning for Switching State-Space Models, Neural Computation 12(4) (1999), 963-996.
7. D. Heckerman, A Tutorial on Learning with Bayesian Networks, Technical Report, MSR-TR-95-06, Microsoft Research, 1996.
8. M. Henrion, Propagating uncertainty in Bayesian networks by probabilistic logic sampling, Proceedings of the 2nd Annual Conference on Uncertainty in AI, 1988, pp. 149-163.
9. Z. Michalewicz and D.B. Fogel, How To Solve It: Modern Heuristics, Springer, 1998.
10. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, 1988.
11. M. Ramoni, P. Sebstiani and P. Cohen, Bayesian Clustering by Dynamics, Machine Learning 47(1) (2002), 91-121.
12. A. Tucker, X. Liu and A. Ogden-Swift, Evolutionary learning of dynamic probabilistic models with large time lags, International Journal of Intelligent Systems 16 (Wiley, 2001), 621-645.
13. A. Tucker, The Automatic Explanation of Multivariate Time Series, PhD Thesis, Birkbeck College, University of London, 2001.
14. N. Ueda and R. Nakano, Deterministic Annealing Variant of the SEM algorithm, Advances in Neural Information Processing Systems 7 (1995), 545-552.
15. M.Wong,W. Lam and S. Leung, Using Evolutionary Programming and Minimum Description Length Principle for Data Mining of Bayesian Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence 21(2) (1999), 174-178.