# Bayesian Learning in a Nonlinear Multiscale State-Space Model

arXiv: https://arxiv.org/abs/2408.06425 · HTML: https://mlsanity.com/paper/2408.06425/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 1 of 1)

- [Particle Gibbs with Ancestor Sampling](https://mlsanity.com/paper/1401.0604/) (2014)

## Built on by (most cited first, 1 of 1)

- [A Generalized Framework for Multiscale State-Space Modeling with Nested Nonlinear Dynamics: An Application to Bayesian Learning under Switching Regimes](https://mlsanity.com/paper/2410.19074/) (2024)

## Abstract (arXiv)

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.