Unbiased estimates for gradients of stochastic network performance measures

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7 Scopus citations

Abstract

Three classes of stochastic networks and their performance measures are considered. These performance measures are defined as the expected value of some random variables and cannot normally be obtained analytically as functions of network parameters in a closed form. We give similar representations for the random variables to provide a useful way of analytical study of these functions and their gradients. The representations are used to obtain sufficient conditions for the gradient estimates to be unbiased. The conditions are rather general and usually met in simulation study of the stochastic networks. Applications of the results are discussed and some practical algorithms of calculating unbiased estimates of the gradients are also presented.
Original languageEnglish
Pages (from-to)21-43
JournalActa Applicandae Mathematicae
Volume33
Issue number1
DOIs
StatePublished - Oct 1993

Scopus subject areas

  • Modelling and Simulation
  • Statistics and Probability

Keywords

  • stochastic network
  • stochastic optimization
  • gradient estimation
  • perturbation analysis

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