Change Detection of Markov Kernels with Unknown Pre and Post Change Kernel
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910384867246080 |
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| author | Chen, Hao Tang, Jiacheng Gupta, Abhishek |
| author_facet | Chen, Hao Tang, Jiacheng Gupta, Abhishek |
| contents | In this paper, we develop a new change detection algorithm for detecting a change in the Markov kernel over a metric space in which the post-change kernel is unknown. Under the assumption that the pre- and post-change Markov kernel is uniformly ergodic, we derive an upper bound on the mean delay and a lower bound on the mean time between false alarms. A numerical simulation is provided to demonstrate the effectiveness of our method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_11722 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Change Detection of Markov Kernels with Unknown Pre and Post Change Kernel Chen, Hao Tang, Jiacheng Gupta, Abhishek Systems and Control Probability Machine Learning In this paper, we develop a new change detection algorithm for detecting a change in the Markov kernel over a metric space in which the post-change kernel is unknown. Under the assumption that the pre- and post-change Markov kernel is uniformly ergodic, we derive an upper bound on the mean delay and a lower bound on the mean time between false alarms. A numerical simulation is provided to demonstrate the effectiveness of our method. |
| title | Change Detection of Markov Kernels with Unknown Pre and Post Change Kernel |
| topic | Systems and Control Probability Machine Learning |
| url | https://arxiv.org/abs/2201.11722 |