A survey of parameter and state estimation in queues

Azam Asanjarani (), Yoni Nazarathy and Peter Taylor
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Azam Asanjarani: The University of Auckland
Yoni Nazarathy: The University of Queensland
Peter Taylor: The University of Melbourne

Queueing Systems: Theory and Applications, 2021, vol. 97, issue 1, No 3, 39-80

Abstract: Abstract We present a broad literature survey of parameter and state estimation for queueing systems. Our approach is based on various inference activities, queueing models, observations schemes, and statistical methods. We categorize these into branches of research that we call estimation paradigms. These include: the classical sampling approach, inverse problems, inference for non-interacting systems, inference with discrete sampling, inference with queueing fundamentals, queue inference engine problems, Bayesian approaches, online prediction, implicit models, and control, design, and uncertainty quantification. For each of these estimation paradigms, we outline the principles and ideas, while surveying key references. We also present various simple numerical experiments. In addition to some key references mentioned here, a periodically updated comprehensive list of references dealing with parameter and state estimation of queues will be kept in an accompanying annotated bibliography.

Keywords: Queueing inference; Queueing parameter estimation; Inverse problems; Queue inference engine; Queueing prediction; 60K25; 68M20; 62M20 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11134-021-09688-w

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