Measure-Valued Differentiation for Markov Chains

  title={Measure-Valued Differentiation for Markov Chains},
  author={Bernd Heidergott and Felisa J. V{\'a}zquez-Abad},
This paper addresses the problem of sensitivity analysis for finite-horizon performance measures of general Markov chains. We derive closed-form expressions and associated unbiased gradient estimators for the derivatives of finite products of Markov kernels by measure-valued differentiation (MVD). In the MVD setting, the derivatives of Markov kernels, called D-derivatives, are defined with respect to a class of performance functions D such that, for any performance measure g ∈ D, the derivative… CONTINUE READING

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