Variational inference and learning for non-linear state-space models with state-dependent observation noise

Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010

Volumn , Issue , 2010, Pages 190-195

  Peltola, Veli ^a   Honkela, Antti ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

AUTO-REGRESSIVE; GAUSSIAN PROCESSES; INHERENT NOISE; LATENT VARIABLE; LINEAR DEPENDENCE; LORENZ SYSTEM; NONLINEAR STATE; NONLINEAR STATE SPACE MODELS; OBSERVATION NOISE; PREDICTIVE PERFORMANCE; STATE-DEPENDENT; STATE-DEPENDENT NOISE; STOCK PRICE PREDICTION; VARIATIONAL INFERENCE;

COMPUTER SIMULATION; DYNAMICAL SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; SIGNAL PROCESSING;

INFERENCE ENGINES;

EID: 78449305564     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2010.5588996     Document Type: Conference Paper

References (17)

1. M. J. Beal, Variational Algorithms for Approximate Bayesian Inference, Ph.D. thesis, University College London, UK, 2003.
2. D. Barber and S. Chiappa, "Unified inference for variational Bayesian linear Gaussian state-space models," in Advances in Neural Information Processing Systems 20, Cambridge, MA, 2007, The MIT Press.
3. H. Valpola and J. Karhunen, "An unsupervised ensemble learning method for nonlinear dynamic state-space models," Neural Computation, vol. 14, no. 11, pp. 2647-2692, 2002.
4. R. Turner, M. P. Deisenroth, and C. Rasmussen, "System identification in Gaussian process dynamical systems," in Nonparametric Bayes Workshop (NIPS 2009), Whistler, BC, Canada, 2009.
5. H. Valpola, M. Harva, and J. Karhunen, "Hierarchical models of variance sources," Signal Processing, vol. 84, no. 2, pp. 267-282, 2004.
6. E. Ghysels, A. C. Harvey, and E. Renault, "Stochastic volatility," in Statistical Methods in Finance, C. R. Rao and G. S. Maddala, Eds., pp. 119-191. North-Holland, Amsterdam, 1996.
7. N. Shepard, "Statistical aspects of ARCH and stochastic volatility," in Time series models in econometrics, finance and other fields, D. R. Cox, D. V. Hinkley, and O. E. Barndorff-Nielson, Eds., pp. 1-67. Chapman & Hall, London, 1996.
8. E. Jacquier, N. G. Polson, and P. E. Rossi, "Bayesian analysis of stochastic volatility models," Journal of Business & Economic Statistics, vol. 12, no. 4, pp. 371-389, 1994.
9. C. Bishop, Pattern Recognition and Machince Learning, Springer, New York, 2006.
10. A. Honkela and H. Valpola, "Unsupervised variational Bayesian learning of nonlinear models," in Advances in Neural Information Processing Systems 17, pp. 593-600. MIT Press, Cambridge, MA, 2005.
11. A. Honkela, M. Tornio, T. Raiko, and J. Karhunen, "Natural conjugate gradient in variational inference," in Proc. 14th Int. Conf. on Neural Information Processing (ICONIP 2007), Kitakyushu, Japan, 2008, vol. 4985 of Lecture Notes in Computer Science, pp. 305-314, Springer-Verlag, Berlin.
12. A. Honkela, H. Valpola, A. Ilin, and J. Karhunen, "Blind separation of nonlinear mixtures by variational Bayesian learning," Digital Signal Processing, vol. 17, no. 5, pp. 914-934, 2007.
13. H. Lappalainen and J. Miskin, "Ensemble learning," in Advances in Independent Component Analysis, M. Girolami, Ed., pp. 75-92. Springer-Verlag, Berlin, 2000.
14. K. Kurihara, M. Welling, and N. Vlassis, "Accelerated variational Dirichlet process mixtures," in Advances in Neural Information Processing Systems 19, pp. 761-768. MIT Press, Cambridge, MA, 2007.
15. A. Girard, C. E. Rasmussen, J. Quiñonero-Candela, and R. Murray-Smith, "Gaussian process priors with uncertain inputs - application to multiple-step ahead time series forecasting," in Advances in Neural Information Processing Systems 15, Cambridge, MA, 2003, pp. 529-536, MIT Press.
16. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning, MIT Press, 2006.
17. A. Honkela, M. Tornio, and T. Raiko, "Variational Bayes for continuous-time nonlinear state-space models," in NIPS*2006 Workshop on Dynamical Systems, Stochastic Processes and Bayesian Inference, Whistler, B.C., 2006.