Finite dimensional filters for random parameter AR models

TitleFinite dimensional filters for random parameter AR models
Publication TypeConference Paper
Year of Publication1997
AuthorsEvans, J., and V. Krishnamurthy
Conference NameAmerican Control Conference, 1997. Proceedings of the 1997
Pagination2836 -2840 vol.5
Date Publishedjun.
Keywordsautoregressive models, autoregressive processes, difference equation, difference equations, discrete-time Kalman filter, filtering theory, finite dimensional filters, Gauss-Markov process, Kalman filters, probability, stochastic AR models, stochastic processes, unnormalized conditional density
Abstract

In this paper exact finite dimensional filters are derived for a class of doubly stochastic autoregressive models. The parameters of the doubly stochastic autoregressive process vary according to a nonlinear function of a Gauss-Markov process. We develop a difference equation for the evolution of an unnormalized conditional density related to the state of the doubly stochastic autoregressive process. We then give a characterization of the general solution followed by examples for which the state of the filter is determined by a finite number of sufficient statistics. These new finite dimensional filters are built upon the discrete-time Kalman filter

URLhttp://dx.doi.org/10.1109/ACC.1997.611973
DOI10.1109/ACC.1997.611973

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