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- W1550687824 abstract "This paper deals with sensitivity analysis (gradient estimation) of random horizon cost functions of Markov chains. More precisely, we consider general state-space Markov chains and the random horizon is given through a hitting time of the chain onto a predened set. The cost of interest is an expectation of a functional of the stopped process. This encompasses a wide range of models, such as the Gambler's ruin problem and performance evaluation for stationary queueing networks. We work within the framework of measure-valued dieren tiation and provide a general condition under which the gradient of the random horizon performance can be obtained in a closed form expression. For several scenarios, which occur typically in applications, we subsequently provide sucien t conditions for our general condition to hold. We illustrate our results with a series of examples. Eventually, we discuss unbiased sensitivity estimators and establish a new unbiased estimator for the gradient of stationary Markov chains." @default.
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- W1550687824 date "2006-01-01" @default.
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- W1550687824 title "Measure valued differentiation for random horizon problems" @default.
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