Auxiliary variables for Bayesian inference in multi-class queueing networks
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...
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| Format: | Article |
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Springer
2017
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| Online Access: | https://eprints.nottingham.ac.uk/47667/ |
| _version_ | 1848797601406648320 |
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| author | Pérez López, Iker Hodge, David Kypraios, Theodore |
| author_facet | Pérez López, Iker Hodge, David Kypraios, Theodore |
| author_sort | Pérez López, Iker |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:06:28Z |
| format | Article |
| id | nottingham-47667 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:06:28Z |
| publishDate | 2017 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-476672020-05-04T19:16:33Z https://eprints.nottingham.ac.uk/47667/ Auxiliary variables for Bayesian inference in multi-class queueing networks Pérez López, Iker Hodge, David Kypraios, Theodore 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. Springer 2017-11-08 Article PeerReviewed 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 Queueing networks Continuous-time Markov Chains Uniformization Markov chain Monte Carlo Slice Sampler https://link.springer.com/article/10.1007%2Fs11222-017-9787-x doi:10.1007/s11222-017-9787-x doi:10.1007/s11222-017-9787-x |
| spellingShingle | Queueing networks Continuous-time Markov Chains Uniformization Markov chain Monte Carlo Slice Sampler Pérez López, Iker Hodge, David Kypraios, Theodore Auxiliary variables for Bayesian inference in multi-class queueing networks |
| title | Auxiliary variables for Bayesian inference in multi-class queueing networks |
| title_full | Auxiliary variables for Bayesian inference in multi-class queueing networks |
| title_fullStr | Auxiliary variables for Bayesian inference in multi-class queueing networks |
| title_full_unstemmed | Auxiliary variables for Bayesian inference in multi-class queueing networks |
| title_short | Auxiliary variables for Bayesian inference in multi-class queueing networks |
| title_sort | auxiliary variables for bayesian inference in multi-class queueing networks |
| topic | Queueing networks Continuous-time Markov Chains Uniformization Markov chain Monte Carlo Slice Sampler |
| url | https://eprints.nottingham.ac.uk/47667/ https://eprints.nottingham.ac.uk/47667/ https://eprints.nottingham.ac.uk/47667/ |