Generalized sensitivity analysis of ergodic stochastic systems

Joseph Kreimer

doi:10.1016/0378-4754(89)90057-8

Generalized sensitivity analysis of ergodic stochastic systems

Joseph Kreimer

Department of Industrial Engineering and Management

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

The tools for sensitivity analysis of stochastic systems whose behavior is described by ergodic stationary processes are presented. Three kinds of generalized estimates for performance sensitivities of these systems are introduced. These estimates, based on a use of time averages, are given in general operator form. Their convergence conditions and rate of convergence are obtained. It is shown that perturbation analysis estimates are generally preferable to crude Monte Carlo estimates.

Original language	English
Pages (from-to)	123-136
Number of pages	14
Journal	Mathematics and Computers in Simulation
Volume	31
Issue number	1-2
DOIs	https://doi.org/10.1016/0378-4754(89)90057-8
State	Published - 1 Jan 1989

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science (all)
Numerical Analysis
Modeling and Simulation
Applied Mathematics

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10.1016/0378-4754(89)90057-8

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abstract = "The tools for sensitivity analysis of stochastic systems whose behavior is described by ergodic stationary processes are presented. Three kinds of generalized estimates for performance sensitivities of these systems are introduced. These estimates, based on a use of time averages, are given in general operator form. Their convergence conditions and rate of convergence are obtained. It is shown that perturbation analysis estimates are generally preferable to crude Monte Carlo estimates.",

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AB - The tools for sensitivity analysis of stochastic systems whose behavior is described by ergodic stationary processes are presented. Three kinds of generalized estimates for performance sensitivities of these systems are introduced. These estimates, based on a use of time averages, are given in general operator form. Their convergence conditions and rate of convergence are obtained. It is shown that perturbation analysis estimates are generally preferable to crude Monte Carlo estimates.

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Generalized sensitivity analysis of ergodic stochastic systems

Abstract

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