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arXiv · 2408.06425

Bayesian Learning in a Nonlinear Multiscale State-Space Model

Nayely Vélez-Cruz·Manfred D. Laubichler
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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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Nayely Vélez-Cruz, Manfred D. Laubichler. 2024-08-12. Bayesian Learning in a Nonlinear Multiscale State-Space Model. https://arxiv.org/abs/2408.06425

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