System identification using particle filters

A.k.a. parameter estimation in data assimilation

Particle filters + system idenfitication.

A placeholder.

State augmentation

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


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


Evensen, Geir. 2009. Data Assimilation - The Ensemble Kalman Filter. Berlin; Heidelberg: Springer.
Fearnhead, Paul, and Hans R. Künsch. 2018. Particle Filters and Data Assimilation.” Annual Review of Statistics and Its Application 5 (1): 421–49.
Frei, Marco, and Hans R. Künsch. 2012. Sequential State and Observation Noise Covariance Estimation Using Combined Ensemble Kalman and Particle Filters.” Monthly Weather Review 140 (5): 1476–95.
Kantas, Nikolas, Arnaud Doucet, Sumeetpal S. Singh, Jan Maciejowski, and Nicolas Chopin. 2015. On Particle Methods for Parameter Estimation in State-Space Models.” Statistical Science 30 (3): 328–51.
Künsch, Hans R. 2013. Particle Filters.” Bernoulli 19 (4): 1391–1403.

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