Bayesian Learning in a Nonlinear Multiscale State-Space Model
<|reference_start|>Bayesian Learning in a Nonlinear Multiscale State-Space Model: 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.<|reference_end|>
@article{vélez-cruz2024bayesian,
    title={Bayesian Learning in a Nonlinear Multiscale State-Space Model},
    author={Nayely V'elez-Cruz and Manfred D. Laubichler},
    journal={arXiv preprint arXiv:2408.06425},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06425},
    primaryClass={eess.SP cs.LG stat.ML}
}