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- W4298302303 abstract "We devise a Monte Carlo based method for detecting whether a non-negative Markov chain is stable for a given set of parameter values. More precisely, for a given subset of the parameter space, we develop an algorithm that is capable of deciding whether the set has a subset of positive Lebesgue measure for which the Markov chain is unstable. The approach is based on a variant of simulated annealing, and consequently only mild assumptions are needed to obtain performance guarantees. The theoretical underpinnings of our algorithm are based on a result stating that the stability of a set of parameters can be phrased in terms of the stability of a single Markov chain that searches the set for unstable parameters. Our framework leads to a procedure that is capable of performing statistically rigorous tests for instability, which has been extensively tested using several examples of standard and non-standard queueing networks." @default.
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- W4298302303 date "2016-08-10" @default.
- W4298302303 modified "2023-09-25" @default.
- W4298302303 title "Detecting Markov Chain Instability: A Monte Carlo Approach" @default.
- W4298302303 doi "https://doi.org/10.48550/arxiv.1608.03257" @default.
- W4298302303 hasPublicationYear "2016" @default.
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