EM++: A parameter learning framework for stochastic switching systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914222297841664 |
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| author | Wang, Renzi Bodard, Alexander Schuurmans, Mathijs Patrinos, Panagiotis |
| author_facet | Wang, Renzi Bodard, Alexander Schuurmans, Mathijs Patrinos, Panagiotis |
| contents | This paper proposes a general switching dynamical system model, and a custom majorization-minimization-based algorithm EM++ for identifying its parameters. For certain families of distributions, such as Gaussian distributions, this algorithm reduces to the well-known expectation-maximization method. We prove global convergence of the algorithm under suitable assumptions, thus addressing an important open issue in the switching system identification literature. The effectiveness of both the proposed model and algorithm is validated through extensive numerical experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_16359 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EM++: A parameter learning framework for stochastic switching systems Wang, Renzi Bodard, Alexander Schuurmans, Mathijs Patrinos, Panagiotis Optimization and Control Systems and Control This paper proposes a general switching dynamical system model, and a custom majorization-minimization-based algorithm EM++ for identifying its parameters. For certain families of distributions, such as Gaussian distributions, this algorithm reduces to the well-known expectation-maximization method. We prove global convergence of the algorithm under suitable assumptions, thus addressing an important open issue in the switching system identification literature. The effectiveness of both the proposed model and algorithm is validated through extensive numerical experiments. |
| title | EM++: A parameter learning framework for stochastic switching systems |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2407.16359 |