This is a partly expository paper discussing how point processes with certain "bursty" features can be qualitatively modelled by the Markovian arrival process, a generalization of the Poisson or Bernoulli processes which can be used to obtain algorithmically tractable matrix solutions to a variety of problems in probability models.
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Marcel F. NEUTS, "Models Based on the Markovian Arrival Process" in IEICE TRANSACTIONS on Communications,
vol. E75-B, no. 12, pp. 1255-1265, December 1992, doi: .
Abstract: This is a partly expository paper discussing how point processes with certain "bursty" features can be qualitatively modelled by the Markovian arrival process, a generalization of the Poisson or Bernoulli processes which can be used to obtain algorithmically tractable matrix solutions to a variety of problems in probability models.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e75-b_12_1255/_p
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@ARTICLE{e75-b_12_1255,
author={Marcel F. NEUTS, },
journal={IEICE TRANSACTIONS on Communications},
title={Models Based on the Markovian Arrival Process},
year={1992},
volume={E75-B},
number={12},
pages={1255-1265},
abstract={This is a partly expository paper discussing how point processes with certain "bursty" features can be qualitatively modelled by the Markovian arrival process, a generalization of the Poisson or Bernoulli processes which can be used to obtain algorithmically tractable matrix solutions to a variety of problems in probability models.},
keywords={},
doi={},
ISSN={},
month={December},}
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TY - JOUR
TI - Models Based on the Markovian Arrival Process
T2 - IEICE TRANSACTIONS on Communications
SP - 1255
EP - 1265
AU - Marcel F. NEUTS
PY - 1992
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E75-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 1992
AB - This is a partly expository paper discussing how point processes with certain "bursty" features can be qualitatively modelled by the Markovian arrival process, a generalization of the Poisson or Bernoulli processes which can be used to obtain algorithmically tractable matrix solutions to a variety of problems in probability models.
ER -