Approximate inference in switching linear dynamical systems using Gaussian mixtures

Bayesian Time Series Models

Volumn 9780521196765, Issue , 2011, Pages 166-181

  Barber, David ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL PLANTS; ECONOMICS; LINEAR CONTROL SYSTEMS; LINEAR SYSTEMS; STATISTICS; TIME SERIES;

APPROXIMATE INFERENCE; COMPLEX TIME SERIES; GAUSSIAN MIXTURES; LATENT VARIABLE; LINEAR DYNAMICAL SYSTEMS; LINEAR PROCESS; PARAMETER SETTING; SWITCHING LINEAR DYNAMICAL SYSTEMS;

DYNAMICAL SYSTEMS;

EID: 84923510314     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9780511984679.009     Document Type: Chapter

References (21)

1. D. L. Alspach and H. W. Sorenson. Nonlinear Bayesian estimation using Gaussian sum approximations. IEEE Transactions on Automatic Control, 17(4):439-448, 1972.
2. Y. Bar-Shalom and Xiao-Rong Li. Estimation and Tracking: Principles, Techniques and Software. Artech House, 1998.
3. D. Barber. Expectation correction for smoothing in switching linear Gaussian state space models. Journal ofMachine Learning Research, 7:2515-2540, 2006.
4. C. Carter and R. Kohn. Markov chain Monte Carlo in conditionally Gaussian state space models. Biometrika, 83:589-601, 1996.
5. A. T. Cemgil, B. Kappen and D. Barber. A generative model for music transcription. IEEE Transactions on Audio, Speech and Language Processing, 14(2):679 - 694, 2006.
6. S. Chib and M. Dueker. Non-Markovian regime switching with endogenous states and time-varying state strengths. Econometric Society 2004 North American Summer Meetings 600, Econometric Society, August 2004.
7. A. Doucet, N. de Freitas, K. Murphy and S. Russell. Rao-Blackwellised particle filtering for dynamic Bayesian networks. Uncertainty in Artificial Intelligence, 2000.
8. S. Frühwirth-Schnatter. Finite Mixture and Markov Switching Models. Springer, 2006.
9. Z. Ghahramani and G. E. Hinton. Variational learning for switching state-space models. Neural Computation, 12(4):963-996, 1998.
10. T. Heskes and O. Zoeter. Expectation propagation for approximate inference in dynamic Bayesian networks. In A. Darwiche and N. Friedman, editors, Uncertainty in Artificial Intelligence, pages 216-223, 2002.
11. C.-J. Kim. Dynamic linear models with Markov-switching. Journal ofEconometrics, 60:1-22, 1994.
12. C.-J. Kim and C. R. Nelson. State-Space Models with Regime Switching. MIT Press, 1999.
13. G. Kitagawa. The two-filter formula for smoothing and an implementation of the Gaussian-sum smoother. Annals of the Institute of Statistical Mathematics, 46(4):605-623, 1994.
14. G. Kitagawa. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5(1):1-25, 1996.
15. U. Lerner, R. Parr, D. Koller and G. Biswas. Bayesian fault detection and diagnosis in dynamic systems. In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AIII-00), pages 531-537, 2000.
16. U. N. Lerner. Hybrid Bayesian networks for reasoning about complex systems. PhD thesis, Stanford University, 2002.
17. B. Mesot and D. Barber. Switching linear dynamical systems for noise robust speech recognition. IEEE Transactions of Audio, Speech and Language Processing, 15(6):1850-1858, 2007.
18. T. Minka. A family of algorithms for approximate Bayesian inference. PhD thesis, MIT Media Lab, 2001.
19. V. Pavlovic, J. M. Rehg and J. MacCormick. Learning switching linear models of human motion. In Advances in Neural Information Processing Systems (NIPS), number 13, pages 981-987, 2001. MIT Press.
20. D. M. Titterington, A. F. M. Smith and U. E. Makov. Statistical Analysis of Finite Mixture Distributions. John Wiley & Sons, 1985.
21. O. Zoeter. Monitoring non-linear and switching dynamical systems. PhD thesis, Radboud University Nijmegen, 2005.