Bayesian Learning in a Nonlinear Multiscale State-Space Model

• URL: http://arxiv.org/abs/2408.06425v6
• Date: Tue, 3 Sep 2024 15:07:13 GMT
• Title: Bayesian Learning in a Nonlinear Multiscale State-Space Model
• Authors: Nayely Vélez-Cruz, Manfred D. Laubichler,
• Abstract summary: This work introduces a novel multiscale state-space model to explore the dynamic interplay between systems interacting across different time scales. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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