Identification of Non-causal Graphical Models
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917801321562112 |
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| author | You, Junyao Zorzi, Mattia |
| author_facet | You, Junyao Zorzi, Mattia |
| contents | The paper considers the problem to estimate non-causal graphical models whose edges encode smoothing relations among the variables. We propose a new covariance extension problem and show that the solution minimizing the transportation distance with respect to white noise process is a double-sided autoregressive non-causal graphical model. Then, we generalize the paradigm to a class of graphical autoregressive moving-average models. Finally, we test the performance of the proposed method through some numerical experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09480 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identification of Non-causal Graphical Models You, Junyao Zorzi, Mattia Machine Learning Optimization and Control The paper considers the problem to estimate non-causal graphical models whose edges encode smoothing relations among the variables. We propose a new covariance extension problem and show that the solution minimizing the transportation distance with respect to white noise process is a double-sided autoregressive non-causal graphical model. Then, we generalize the paradigm to a class of graphical autoregressive moving-average models. Finally, we test the performance of the proposed method through some numerical experiments. |
| title | Identification of Non-causal Graphical Models |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2410.09480 |