# System identification using particle filters

A.k.a. parameter estimation in data assimilation

July 25, 2014 — May 4, 2022

Bayes

Monte Carlo

probabilistic algorithms

probability

sciml

signal processing

state space models

statistics

time series

Particle filters + system idenfitication.

A placeholder.

## 1 State augmentation

The classic; just include the parameter vector in the state vector and give it a “small” magnitude random evolution. (But how small?)

## 2 Via MCMC

a.k.a. particle MCMC. See Frei and Künsch (2012). Kantas et al. (2015) and Fearnhead and Künsch (2018) introduce more.

## 3 References

Evensen. 2009.

*Data Assimilation - The Ensemble Kalman Filter*.
Fearnhead, and Künsch. 2018. “Particle Filters and Data Assimilation.”

*Annual Review of Statistics and Its Application*.
Frei, and Künsch. 2012. “Sequential State and Observation Noise Covariance Estimation Using Combined Ensemble Kalman and Particle Filters.”

*Monthly Weather Review*.
Kantas, Doucet, Singh, et al. 2015. “On Particle Methods for Parameter Estimation in State-Space Models.”

*Statistical Science*.
Künsch. 2013. “Particle Filters.”

*Bernoulli*.
Lindsten. 2011. “Rao-Blackwellised Particle Methods for Inference and Identification.”

Liu, Zhuo, Cheng, et al. 2019. “Understanding and Accelerating Particle-Based Variational Inference.” In

*Proceedings of the 36th International Conference on Machine Learning*.
Maurais, and Marzouk. 2024. “Sampling in Unit Time with Kernel Fisher-Rao Flow.”