Runggaldier, W.J. & Visentin, C. (1987). Combined Filtering and Parameter Estimation for DiscreteTime Systems Driven by Approximately White Gaussian Noise Disturbances. IIASA Working Paper. IIASA, Laxenburg, Austria: WP87060

Text
WP87060.pdf Download (411kB)  Preview 
Abstract
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 linearGaussian model so that the filtering problem for x_t can be solved via the familiar KalmanBucy 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.
Item Type:  Monograph (IIASA Working Paper) 

Research Programs:  Adaption and Optimization (ADO) 
Depositing User:  IIASA Import 
Date Deposited:  15 Jan 2016 01:58 
Last Modified:  27 Aug 2021 17:13 
URI:  https://pure.iiasa.ac.at/2992 
Actions (login required)
View Item 