Computing inferences for large-scale continuous-time Markov chains by combining lumping with imprecision
- Author
- Alexander Erreygers (UGent) and Jasper De Bock (UGent)
- Organization
- Abstract
- If the state space of a homogeneous continuous-time Markov chain is too large, making inferences—here limited to determining marginal or limit expectations—becomes computationally infeasible. Fortunately, the state space of such a chain is usually too detailed for the inferences we are interested in, in the sense that a less detailed—smaller—state space suffices to unambiguously formalise the inference. However, in general this so-called lumped state space inhibits computing exact inferences because the corresponding dynamics are unknown and/or intractable to obtain. We address this issue by considering an imprecise continuous-time Markov chain. In this way, we are able to provide guaranteed lower and upper bounds for the inferences of interest, without suffering from the curse of dimensionality.
- Keywords
- BOUNDS
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8575677
- MLA
- Erreygers, Alexander, and Jasper De Bock. “Computing Inferences for Large-Scale Continuous-Time Markov Chains by Combining Lumping with Imprecision.” Uncertainty Modelling in Data Science, edited by Sébastien Destercke et al., vol. 832, Springer International Publishing, 2019, pp. 78–86, doi:10.1007/978-3-319-97547-4_11.
- APA
- Erreygers, A., & De Bock, J. (2019). Computing inferences for large-scale continuous-time Markov chains by combining lumping with imprecision. In S. Destercke, T. Denoeux, M. Á. Gil, P. Gregorzewski, & O. Hryniewicz (Eds.), Uncertainty Modelling in Data Science (Vol. 832, pp. 78–86). https://doi.org/10.1007/978-3-319-97547-4_11
- Chicago author-date
- Erreygers, Alexander, and Jasper De Bock. 2019. “Computing Inferences for Large-Scale Continuous-Time Markov Chains by Combining Lumping with Imprecision.” In Uncertainty Modelling in Data Science, edited by Sébastien Destercke, Thierry Denoeux, María Ángeles Gil, Przemyslaw Gregorzewski, and Olgierd Hryniewicz, 832:78–86. Cham, Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-97547-4_11.
- Chicago author-date (all authors)
- Erreygers, Alexander, and Jasper De Bock. 2019. “Computing Inferences for Large-Scale Continuous-Time Markov Chains by Combining Lumping with Imprecision.” In Uncertainty Modelling in Data Science, ed by. Sébastien Destercke, Thierry Denoeux, María Ángeles Gil, Przemyslaw Gregorzewski, and Olgierd Hryniewicz, 832:78–86. Cham, Switzerland: Springer International Publishing. doi:10.1007/978-3-319-97547-4_11.
- Vancouver
- 1.Erreygers A, De Bock J. Computing inferences for large-scale continuous-time Markov chains by combining lumping with imprecision. In: Destercke S, Denoeux T, Gil MÁ, Gregorzewski P, Hryniewicz O, editors. Uncertainty Modelling in Data Science. Cham, Switzerland: Springer International Publishing; 2019. p. 78–86.
- IEEE
- [1]A. Erreygers and J. De Bock, “Computing inferences for large-scale continuous-time Markov chains by combining lumping with imprecision,” in Uncertainty Modelling in Data Science, Compiègne, France, 2019, vol. 832, pp. 78–86.
@inproceedings{8575677,
abstract = {{If the state space of a homogeneous continuous-time Markov chain is too large, making inferences—here limited to determining marginal or limit expectations—becomes computationally infeasible. Fortunately, the state space of such a chain is usually too detailed for the inferences we are interested in, in the sense that a less detailed—smaller—state space suffices to unambiguously formalise the inference. However, in general this so-called lumped state space inhibits computing exact inferences because the corresponding dynamics are unknown and/or intractable to obtain. We address this issue by considering an imprecise continuous-time Markov chain. In this way, we are able to provide guaranteed lower and upper bounds for the inferences of interest, without suffering from the curse of dimensionality.}},
author = {{Erreygers, Alexander and De Bock, Jasper}},
booktitle = {{Uncertainty Modelling in Data Science}},
editor = {{Destercke, Sébastien and Denoeux, Thierry and Gil, María Ángeles and Gregorzewski, Przemyslaw and Hryniewicz, Olgierd}},
isbn = {{9783319975467}},
issn = {{2194-5357}},
keywords = {{BOUNDS}},
language = {{eng}},
location = {{Compiègne, France}},
pages = {{78--86}},
publisher = {{Springer International Publishing}},
title = {{Computing inferences for large-scale continuous-time Markov chains by combining lumping with imprecision}},
url = {{http://doi.org/10.1007/978-3-319-97547-4_11}},
volume = {{832}},
year = {{2019}},
}
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