Particle filter as a controlled Markov chain for on-line parameter estimation in general state space models

Poyiadjis, G; Singh, SS; Doucet, A; IEEE

Abstract:

In this paper we present a novel optimization method for on-line maximum likelihood estimation (MLE) of the static parameters of a general state space model. Our approach is based on viewing the particle filter as a controlled Markov chain, where the control is the unknown static parameters to be identified, The algorithm relies on the computation of the gradient of the particle filter using a score function approach. © 2006 IEEE.

Publication status:

Published

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APA Style

Poyiadjis, G., Singh, S. S., Doucet, A., & IEEE. (2006). Particle filter as a controlled Markov chain for on-line parameter estimation in general state space models. 3, 329-+.

MLA Style

Poyiadjis, G., et al. Particle Filter as a Controlled Markov Chain for on-Line Parameter Estimation in General State Space Models. 2006, pp. 329-+.

Chicago Style

Poyiadjis, G, SS Singh, A Doucet, and IEEE. 2006. “Particle Filter as a Controlled Markov Chain for on-Line Parameter Estimation in General State Space Models.” In , 3:329-+.
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