# Sequential Monte Carlo Methods for System Identification

arXiv: https://arxiv.org/abs/1503.06058 · HTML: https://mlsanity.com/paper/1503.06058/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2015.

## Built on by (most cited first, 6 of 6)

- [Deep networks for system identification: a Survey](https://mlsanity.com/paper/2301.12832/) (2023)
- [Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems](https://mlsanity.com/paper/2305.01773/) (2023)
- [Variational message passing for online polynomial NARMAX identification](https://mlsanity.com/paper/2204.00769/) (2022)
- [DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical models](https://mlsanity.com/paper/2205.02264/) (2022)
- [Learning linear modules in a dynamic network with missing node observations](https://mlsanity.com/paper/2208.10995/) (2022)
- [LILAD: Learning In-context Lyapunov-stable Adaptive Dynamics Models](https://mlsanity.com/paper/2511.21846/) (2025)

## Abstract (arXiv)

One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced more than two decades ago), provide numerical solutions to the nonlinear state estimation problems arising in SSMs. When combined with additional identification techniques, these algorithms provide solid solutions to the nonlinear system identification problem. We describe two general strategies for creating such combinations and discuss why SMC is a natural tool for implementing these strategies.