Enhancing Computational Efficiency in State-Space Models Using Rao-Blackwellization and 2-Step Approximation
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916494256898048 |
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| author | Kitagawa, Genshiro |
| author_facet | Kitagawa, Genshiro |
| contents | This paper explores a Bayesian self-organization method for state-space models, enabling simultaneous state and parameter estimation without repeated likelihood calculations. While efficient for low-dimensional models, high-dimensional cases like seasonal adjustment require many particles. Using Rao-Blackwellization and a 2-step approximation, the method reduces particle use and computation time while maintaining accuracy, as shown in Monte Carlo evaluations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16056 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Computational Efficiency in State-Space Models Using Rao-Blackwellization and 2-Step Approximation Kitagawa, Genshiro Computation 62M20(Primary), 62M05(Secondary) This paper explores a Bayesian self-organization method for state-space models, enabling simultaneous state and parameter estimation without repeated likelihood calculations. While efficient for low-dimensional models, high-dimensional cases like seasonal adjustment require many particles. Using Rao-Blackwellization and a 2-step approximation, the method reduces particle use and computation time while maintaining accuracy, as shown in Monte Carlo evaluations. |
| title | Enhancing Computational Efficiency in State-Space Models Using Rao-Blackwellization and 2-Step Approximation |
| topic | Computation 62M20(Primary), 62M05(Secondary) |
| url | https://arxiv.org/abs/2411.16056 |