Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866916316738224128 |
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| author | Chen, Dawei Yang-Zhao, Samuel Lloyd, John Ng, Kee Siong |
| author_facet | Chen, Dawei Yang-Zhao, Samuel Lloyd, John Ng, Kee Siong |
| contents | This paper introduces factored conditional filters, new filtering algorithms for simultaneously tracking states and estimating parameters in high-dimensional state spaces. The conditional nature of the algorithms is used to estimate parameters and the factored nature is used to decompose the state space into low-dimensional subspaces in such a way that filtering on these subspaces gives distributions whose product is a good approximation to the distribution on the entire state space. The conditions for successful application of the algorithms are that observations be available at the subspace level and that the transition model can be factored into local transition models that are approximately confined to the subspaces; these conditions are widely satisfied in computer science, engineering, and geophysical filtering applications. We give experimental results on tracking epidemics and estimating parameters in large contact networks that show the effectiveness of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_02178 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces Chen, Dawei Yang-Zhao, Samuel Lloyd, John Ng, Kee Siong Artificial Intelligence Machine Learning 68T37 I.2.6 This paper introduces factored conditional filters, new filtering algorithms for simultaneously tracking states and estimating parameters in high-dimensional state spaces. The conditional nature of the algorithms is used to estimate parameters and the factored nature is used to decompose the state space into low-dimensional subspaces in such a way that filtering on these subspaces gives distributions whose product is a good approximation to the distribution on the entire state space. The conditions for successful application of the algorithms are that observations be available at the subspace level and that the transition model can be factored into local transition models that are approximately confined to the subspaces; these conditions are widely satisfied in computer science, engineering, and geophysical filtering applications. We give experimental results on tracking epidemics and estimating parameters in large contact networks that show the effectiveness of our approach. |
| title | Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces |
| topic | Artificial Intelligence Machine Learning 68T37 I.2.6 |
| url | https://arxiv.org/abs/2206.02178 |