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

Record type: Article

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.

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Citation

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), pp. 559-582. (doi:10.1016/0165-1889(95)00864-0).

More information

Published date: 1996
Keywords: dynamic discrete time system, optimal control, inverse control problem, maximum entropy principle, time series data

Identifiers

Local EPrints ID: 32967
URI: http://eprints.soton.ac.uk/id/eprint/32967
ISSN: 0165-1889
PURE UUID: 719a9f88-4707-4c6b-870c-6f2adebab8f2

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Date deposited: 05 Jul 2007
Last modified: 17 Jul 2017 15:54

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Contributors

Author: G. Amos
Author: G. Judge
Author: L. Karp

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