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Computing the expected Markov reward rates with stationarity detection and relative error control

Autor
Suñe, V.
Tipus d'activitat
Article en revista
Revista
Methodology and computing in applied probability
Data de publicació
2017-06
Volum
19
Número
2
Pàgina inicial
445
Pàgina final
485
DOI
https://doi.org/10.1007/s11009-016-9490-y Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/85787 Obrir en finestra nova
URL
https://link.springer.com/article/10.1007%2Fs11009-016-9490-y Obrir en finestra nova
Resum
By combining in a novel way the randomization method with the stationary detection technique, we develop two new algorithms for the computation of the expected reward rates of finite, irreducible Markov reward models, with control of the relative error. The first algorithm computes the expected transient reward rate and the second one computes the expected averaged reward rate. The algorithms are numerically stable. Further, it is argued that, from the point of view of run-time computational cos...
Citació
Suñe, V. Computing the expected Markov reward rates with stationarity detection and relative error control. "Methodology and computing in applied probability", 11 Març 2016, p. 1-41.
Paraules clau
Markov Reward Model, Markov Chain, Expected Reward Rate, Relative Error, Randomization, Stationarity Detection

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