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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/3041 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/3041 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/3041 archive 351 disk0/00/00/29/92 2016-01-15 01:58:02 2021-08-27 17:13:01 2016-01-15 01:58:02 monograph show 3 Runggaldier W.J. AL1166 Visentin C. Combined Filtering and Parameter Estimation for Discrete-Time Systems Driven by Approximately White Gaussian Noise Disturbances pub iis_sys iis_met iis_mod prog_ado In the problem of combined filtering and parameter estimation one considers a stochastic dynamical system whose state x_t is only partially observed through an observation process y_t. The stochastic model for the process pair (x_t, y_t) depends furthermore on an unknown parameter theta. Given an observation history of the process y_t, the problem then consists in estimating recursively both the current state x_t of the system (filtering) as well as the value theta of the parameter (Bayesian parameter estimation). The problem is a rather difficult one: Even if, conditionally on a given value of theta, the process pair (x_t, y_t) satisfies a linear-Gaussian model so that the filtering problem for x_t can be solved via the familiar Kalman-Bucy filter; when theta is unknown, the problem becomes a difficult nonlinear filtering problem. The present paper, partly based on previous joint work of one of the authors, makes a contribution towards the solution of this problem in the case of discrete time and of a (conditionally on theta) linear model for x_t, y_t. The solution that is obtained is shown to be robust with respect to small variations in the a priori distributions in the model, in particular those of the disturbances. 1987-07 published WP-87-060 WP-87-060 10 2346 public working_paper IIASA, Laxenburg, Austria 20 FALSE info:eu-repo/semantics/book