Bayesian Learning in a Nonlinear Multiscale State-Space Model
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909303012589568 |
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| author | Vélez-Cruz, Nayely Laubichler, Manfred D. |
| author_facet | Vélez-Cruz, Nayely Laubichler, Manfred D. |
| contents | The ubiquity of multiscale interactions in complex systems is well-recognized, with development and heredity serving as a prime example of how processes at different temporal scales influence one another. This work introduces a novel multiscale state-space model to explore the dynamic interplay between systems interacting across different time scales, with feedback between each scale. We propose a Bayesian learning framework to estimate unknown states by learning the unknown process noise covariances within this multiscale model. We develop a Particle Gibbs with Ancestor Sampling (PGAS) algorithm for inference and demonstrate through simulations the efficacy of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06425 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian Learning in a Nonlinear Multiscale State-Space Model Vélez-Cruz, Nayely Laubichler, Manfred D. Signal Processing Machine Learning The ubiquity of multiscale interactions in complex systems is well-recognized, with development and heredity serving as a prime example of how processes at different temporal scales influence one another. This work introduces a novel multiscale state-space model to explore the dynamic interplay between systems interacting across different time scales, with feedback between each scale. We propose a Bayesian learning framework to estimate unknown states by learning the unknown process noise covariances within this multiscale model. We develop a Particle Gibbs with Ancestor Sampling (PGAS) algorithm for inference and demonstrate through simulations the efficacy of our approach. |
| title | Bayesian Learning in a Nonlinear Multiscale State-Space Model |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2408.06425 |