Journal article

Online Expectation-Maximization type algorithms for parameter estimation in general state space models

Abstract:: In this paper we present new online algorithms to estimate static parameters in nonlinear non Gaussian state space models. These algorithms rely on online Expectation-Maximization (EM) type algorithms. Contrary to standard Sequential Monte Carlo (SMC) methods recently proposed in the literature, these algorithms do not degenerate over time.

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APA Style

Andrieu, C., & Doucet, A. (2003). Online Expectation-Maximization type algorithms for parameter estimation in general state space models. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 6, 69–72.

MLA Style

Andrieu, C, and A Doucet. “Online Expectation-Maximization Type Algorithms for Parameter Estimation in General State Space Models.” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 6, 2003, pp. 69–72.

Chicago Style

Andrieu, C, and A Doucet. 2003. “Online Expectation-Maximization Type Algorithms for Parameter Estimation in General State Space Models.” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 6: 69–72.
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Authors

+ Andrieu, C More by this author

Role:: Author

+ Doucet, A More by this author

Role:: Author

Journal:: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings More from this journal
Volume:: 6
Pages:: 69-72
Publication date:: 2003-01-01
ISSN:: 1520-6149

Language:: English
Pubs id:: pubs:190643
UUID:: uuid:0f78343e-1e2e-47f7-8196-f004b5d53fe3
Local pid:: pubs:190643
Source identifiers:: 190643
Deposit date:: 2012-12-19
ARK identifier:: ark:/29072/ora_0f78343e1e2e47f78196f004b5d53fe3

Terms of use

Copyright date:: 2003

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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