Bayesian estimation of coupling strength and heterogeneity in a coupled oscillator model from macroscopic quantities
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915364732928000 |
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| author | Kato, Yusuke Kashiwamura, Shuhei Watanabe, Emiri Okada, Masato Kori, Hiroshi |
| author_facet | Kato, Yusuke Kashiwamura, Shuhei Watanabe, Emiri Okada, Masato Kori, Hiroshi |
| contents | Various macroscopic oscillations, such as the heartbeat and the flashing of fireflies, are created by synchronizing oscillatory units (oscillators). To elucidate the mechanism of synchronization, several coupled oscillator models have been devised and extensively analyzed. Although parameter estimation of these models has also been actively investigated, most of the proposed methods are based on the data from individual oscillators, not from macroscopic quantities. In the present study, we propose a Bayesian framework to estimate the model parameters of coupled oscillator models, using the time series data of the Kuramoto order parameter as the only given data. We adopt the exchange Monte Carlo method for the efficient estimation of the posterior distribution and marginal likelihood. Numerical experiments are performed to confirm the validity of our method and examine the dependence of the estimation error on the observational noise and system size. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07977 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian estimation of coupling strength and heterogeneity in a coupled oscillator model from macroscopic quantities Kato, Yusuke Kashiwamura, Shuhei Watanabe, Emiri Okada, Masato Kori, Hiroshi Data Analysis, Statistics and Probability Adaptation and Self-Organizing Systems Various macroscopic oscillations, such as the heartbeat and the flashing of fireflies, are created by synchronizing oscillatory units (oscillators). To elucidate the mechanism of synchronization, several coupled oscillator models have been devised and extensively analyzed. Although parameter estimation of these models has also been actively investigated, most of the proposed methods are based on the data from individual oscillators, not from macroscopic quantities. In the present study, we propose a Bayesian framework to estimate the model parameters of coupled oscillator models, using the time series data of the Kuramoto order parameter as the only given data. We adopt the exchange Monte Carlo method for the efficient estimation of the posterior distribution and marginal likelihood. Numerical experiments are performed to confirm the validity of our method and examine the dependence of the estimation error on the observational noise and system size. |
| title | Bayesian estimation of coupling strength and heterogeneity in a coupled oscillator model from macroscopic quantities |
| topic | Data Analysis, Statistics and Probability Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2501.07977 |