Nonlinear and non-gaussian state estimation: A quasi-optimal estimator
نویسندگان
چکیده
منابع مشابه
Nonlinear and Non-gaussian State Estimation: a Quasi-optimal Estimator
The rejection sampling filter and smoother, proposed by Tanizaki (1996, 1999), Tanizaki and Mariano (1998) and Hürzeler and Künsch (1998), take a lot of time computationally. The Markov chain Monte Carlo smoother, developed by Carlin, Polson and Stoffer (1992), Carter and Kohn (1994, 1996) and Geweke and Tanizaki (1999a, 1999b), does not show a good performance depending on nonlinearity and non...
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Many systems in the real world are more accurately described by nonlinear models. Since the original work of Kalman (Kalman, 1960; Kalman & Busy, 1961), which introduces the Kalman filter for linear models, extensive research has been going on state estimation of nonlinear models; but there do not yet exist any optimum estimation approaches for all nonlinear models, except for certain classes o...
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For the last decade, various simulation-based nonlinear and non-Gaussian filters and smoothers have been proposed. In the case where the unknown parameters are included in the nonlinear and non-Gaussian system, however, it is very difficult to estimate the parameters together with the state variables, because the state-space model includes a lot of parameters in general and and the simulation-b...
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ژورنال
عنوان ژورنال: Communications in Statistics - Theory and Methods
سال: 2000
ISSN: 0361-0926,1532-415X
DOI: 10.1080/03610920008832638