Imprecise continuous-time Markov chains : efficient computational methods with guaranteed error bounds
- Author
- Alexander Erreygers (UGent) and Jasper De Bock (UGent)
- Organization
- Abstract
- Imprecise continuous-time Markov chains are a robust type of continuous-time Markov chains that allow for partially specified time-dependent parameters. Computing inferences for them requires the solution of a non-linear differential equation. As there is no general analytical expression for this solution, efficient numerical approximation methods are essential to the applicability of this model. We here improve the uniform approximation method of Krak et al. (2016) in two ways and propose a novel and more efficient adaptive approximation method. For ergodic chains, we also provide a method that allows us to approximate stationary distributions up to any desired maximal error.
- Keywords
- imprecise continuous-time Markov chain, lower transition operator, lower transition rate operator, approximation method, ergodicity, coefficient of ergodicity
Downloads
-
erreygers17.pdf
- full text
- |
- open access
- |
- |
- 235.01 KB
-
201707ISIPTA comp poster.pdf
- full text
- |
- open access
- |
- |
- 140.68 KB
-
201707ISIPTA comp slides.pdf
- full text
- |
- open access
- |
- |
- 620.94 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8518302
- MLA
- Erreygers, Alexander, and Jasper De Bock. “Imprecise Continuous-Time Markov Chains : Efficient Computational Methods with Guaranteed Error Bounds.” PMLR : Proceedings of Machine Learning Research, edited by Allesandro Antonucci et al., vol. 62, 2017, pp. 145–56.
- APA
- Erreygers, A., & De Bock, J. (2017). Imprecise continuous-time Markov chains : efficient computational methods with guaranteed error bounds. In A. Antonucci, G. Corani, S. Destercke, & I. Couso (Eds.), PMLR : Proceedings of Machine Learning Research (Vol. 62, pp. 145–156). Lugano, Switzerland.
- Chicago author-date
- Erreygers, Alexander, and Jasper De Bock. 2017. “Imprecise Continuous-Time Markov Chains : Efficient Computational Methods with Guaranteed Error Bounds.” In PMLR : Proceedings of Machine Learning Research, edited by Allesandro Antonucci, Giorgio Corani, Sébastien Destercke, and Inés Couso, 62:145–56. Lugano, Switzerland.
- Chicago author-date (all authors)
- Erreygers, Alexander, and Jasper De Bock. 2017. “Imprecise Continuous-Time Markov Chains : Efficient Computational Methods with Guaranteed Error Bounds.” In PMLR : Proceedings of Machine Learning Research, ed by. Allesandro Antonucci, Giorgio Corani, Sébastien Destercke, and Inés Couso, 62:145–156. Lugano, Switzerland.
- Vancouver
- 1.Erreygers A, De Bock J. Imprecise continuous-time Markov chains : efficient computational methods with guaranteed error bounds. In: Antonucci A, Corani G, Destercke S, Couso I, editors. PMLR : Proceedings of Machine Learning Research. Lugano, Switzerland; 2017. p. 145–56.
- IEEE
- [1]A. Erreygers and J. De Bock, “Imprecise continuous-time Markov chains : efficient computational methods with guaranteed error bounds,” in PMLR : Proceedings of Machine Learning Research, Lugano, Switzerland, 2017, vol. 62, pp. 145–156.
@inproceedings{8518302,
abstract = {{Imprecise continuous-time Markov chains are a robust type of continuous-time Markov chains that allow for partially specified time-dependent parameters. Computing inferences for them requires the solution of a non-linear differential equation. As there is no general analytical expression for this solution, efficient numerical approximation methods are essential to the applicability of this model. We here improve the uniform approximation method of Krak et al. (2016) in two ways and propose a novel and more efficient adaptive approximation method. For ergodic chains, we also provide a method that allows us to approximate stationary distributions up to any desired maximal error.}},
author = {{Erreygers, Alexander and De Bock, Jasper}},
booktitle = {{PMLR : Proceedings of Machine Learning Research}},
editor = {{Antonucci, Allesandro and Corani, Giorgio and Destercke, Sébastien and Couso, Inés}},
issn = {{1938-7228}},
keywords = {{imprecise continuous-time Markov chain,lower transition operator,lower transition rate operator,approximation method,ergodicity,coefficient of ergodicity}},
language = {{eng}},
location = {{Lugano, Switzerland}},
pages = {{145--156}},
title = {{Imprecise continuous-time Markov chains : efficient computational methods with guaranteed error bounds}},
url = {{http://proceedings.mlr.press/v62/erreygers17a/erreygers17a.pdf}},
volume = {{62}},
year = {{2017}},
}