Auxiliary variables for Bayesian inference in multi-class queueing networks

Pérez López, Iker, Hodge, David and Kypraios, Theodore (2017) Auxiliary variables for Bayesian inference in multi-class queueing networks. Statistics and Computing . ISSN 1573-1375

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Queueing networks describe complex stochastic systems of both theoretical and practical interest. They provide the means to assess alterations, diagnose poor performance and evaluate robustness across sets of interconnected resources. In the present paper, we focus on the underlying continuous-time Markov chains induced by these networks, and we present a flexible method for drawing parameter inference in multi-class Markovian cases with switching and different service disciplines. The approach is directed towards the inferential problem with missing data, where transition paths of individual tasks among the queues are often unknown. The paper introduces a slice sampling technique with mappings to the measurable space of task transitions between the service stations. This can address time and tractability issues in computational procedures, handle prior system knowledge and overcome common restrictions on service rates across existing inferential frameworks. Finally, the proposed algorithm is validated on synthetic data and applied to a real data set, obtained from a service delivery tasking tool implemented in two university hospitals.

Item Type: Article
Additional Information: The final publication is available at Springer via
Keywords: Queueing networks, Continuous-time Markov Chains, Uniformization, Markov chain Monte Carlo, Slice Sampler
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
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Depositing User: Eprints, Support
Date Deposited: 01 Nov 2017 10:59
Last Modified: 04 May 2020 19:16

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