Uncovering the topology of an infinite-server queueing network from population data
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915333057544192 |
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| author | Gupta, Hritika Mandjes, Michel Ravner, Liron Wang, Jiesen |
| author_facet | Gupta, Hritika Mandjes, Michel Ravner, Liron Wang, Jiesen |
| contents | This paper studies statistical inference in a network of infinite-server queues, with the aim of estimating the underlying parameters (routing matrix, arrival rates, parameters pertaining to the service times) using observations of the network population vector at Poisson time points. We propose a method-of-moments estimator and establish its consistency. The method relies on deriving the covariance structure of different nodes at different sampling epochs. Numerical experiments demonstrate that the method yields accurate estimates, even in settings with a large number of parameters. Two model variants are considered: one that assumes a known parametric form for the service-time distributions, and a model-free version that does not require such assumptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07057 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncovering the topology of an infinite-server queueing network from population data Gupta, Hritika Mandjes, Michel Ravner, Liron Wang, Jiesen Probability Statistics Theory Methodology This paper studies statistical inference in a network of infinite-server queues, with the aim of estimating the underlying parameters (routing matrix, arrival rates, parameters pertaining to the service times) using observations of the network population vector at Poisson time points. We propose a method-of-moments estimator and establish its consistency. The method relies on deriving the covariance structure of different nodes at different sampling epochs. Numerical experiments demonstrate that the method yields accurate estimates, even in settings with a large number of parameters. Two model variants are considered: one that assumes a known parametric form for the service-time distributions, and a model-free version that does not require such assumptions. |
| title | Uncovering the topology of an infinite-server queueing network from population data |
| topic | Probability Statistics Theory Methodology |
| url | https://arxiv.org/abs/2506.07057 |