A maximum entropy approach to estimation and inference in dynamic models or counting fish in the sea using maximum entropy


Amos, G., Judge, G. and Karp, L. (1996) A maximum entropy approach to estimation and inference in dynamic models or counting fish in the sea using maximum entropy. Journal of Economic Dynamics and Control, 20, (4), 559-582. (doi:10.1016/0165-1889(95)00864-0).

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Description/Abstract

In this paper we consider estimation problems based on dynamic discrete time models. The first problem involves noisy state observations, where the state equation and the observation equation are nonlinear. The objective is to estimate the unknown parameters of the state and observation equations and the unknown values of the state variable. Next we consider the problem of estimating the parameters of the objective function and of the state equation in a linear-quadratic control problem. In each case, given time series observations, we suggest a nonlinear inversion procedure that permits the unknown underlying parameters to be estimated. Examples are presented to suggest the operational nature of the results.

Item Type: Article
ISSNs: 0165-1889 (print)
Related URLs:
Keywords: dynamic discrete time system, optimal control, inverse control problem, maximum entropy principle, time series data
Subjects: H Social Sciences > HB Economic Theory
H Social Sciences > HA Statistics
Divisions: University Structure - Pre August 2011 > School of Social Sciences > Economics
ePrint ID: 32967
Date Deposited: 05 Jul 2007
Last Modified: 27 Mar 2014 18:20
URI: http://eprints.soton.ac.uk/id/eprint/32967

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