Skip to main content

Research Repository

Advanced Search

Auxiliary variables for Bayesian inference in multi-class queueing networks



David Hodge


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.


Pérez López, I., Hodge, D., & Kypraios, T. (2018). Auxiliary variables for Bayesian inference in multi-class queueing networks. Statistics and Computing, 28(6), 1187-1200.

Journal Article Type Article
Acceptance Date Oct 25, 2017
Online Publication Date Nov 8, 2017
Publication Date 2018-11
Deposit Date Nov 1, 2017
Publicly Available Date Nov 9, 2018
Journal Statistics and Computing
Print ISSN 0960-3174
Electronic ISSN 1573-1375
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 28
Issue 6
Pages 1187-1200
Keywords Queueing networks, Continuous-time Markov Chains, Uniformization, Markov chain Monte Carlo, Slice Sampler
Public URL
Publisher URL
Additional Information The final publication is available at Springer via


You might also like

Downloadable Citations