Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916471029891072 |
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| author | Słupiński, Mikołaj Lipiński, Piotr |
| author_facet | Słupiński, Mikołaj Lipiński, Piotr |
| contents | In this paper, we propose a novel model called Recurrent Explicit Duration Switching Linear Dynamical Systems (REDSLDS) that incorporates recurrent explicit duration variables into the rSLDS model. We also propose an inference and learning scheme that involves the use of Pólya-gamma augmentation. We demonstrate the improved segmentation capabilities of our model on three benchmark datasets, including two quantitative datasets and one qualitative dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04280 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems Słupiński, Mikołaj Lipiński, Piotr Machine Learning Artificial Intelligence Dynamical Systems In this paper, we propose a novel model called Recurrent Explicit Duration Switching Linear Dynamical Systems (REDSLDS) that incorporates recurrent explicit duration variables into the rSLDS model. We also propose an inference and learning scheme that involves the use of Pólya-gamma augmentation. We demonstrate the improved segmentation capabilities of our model on three benchmark datasets, including two quantitative datasets and one qualitative dataset. |
| title | Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems |
| topic | Machine Learning Artificial Intelligence Dynamical Systems |
| url | https://arxiv.org/abs/2411.04280 |