Optimal Control and Stochastic Parameter Estimation

Ngnepieba Pierre, Hussaini M. Y. and Debreu Laurent
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Ngnepieba Pierre: 1. Department of Mathematics, Florida A&M University, Tallahassee, Florida 32307, USA
Hussaini M. Y.: 2. School of Computational Science, Florida State University, Tallahassee, Florida 32306-4120, USA
Debreu Laurent: 2. School of Computational Science, Florida State University, Tallahassee, Florida 32306-4120, USA

Monte Carlo Methods and Applications, 2006, vol. 12, issue 5, 461-476

Abstract: An efficient sampling method is proposed to solve the stochastic optimal control problem in the context of data assimilation for the estimation of a random parameter. It is based on Bayesian inference and the Markov Chain Monte Carlo technique, which exploits the relation between the inverse Hessian of the cost function and the error covariance matrix to accelerate convergence of the sampling method. The efficiency and accuracy of the method is demonstrated in the case of the optimal control problem governed by the nonlinear Burgers equation with a viscosity parameter that is a random field.

Keywords: Monte Carlo method; covariance matrix; Hessian matrix; Bayesian inference; Burgers equation. (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (1)

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DOI: 10.1515/156939606779329062

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