Bayesian methodsBayesian / computational
Kalman Filter with Measurement Error
The Kalman filter with measurement error is a recursive Bayesian state-space algorithm that estimates the true hidden state of a dynamic system from noisy observations. It explicitly separates process noise (system dynamics uncertainty) from measurement noise (observation uncertainty), propagating both sources of error through a two-step predict-update cycle to yield optimal filtered state estimates and their associated uncertainty.
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Sources
- Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45. DOI: 10.1115/1.3662552 ↗
- Durbin, J. & Koopman, S. J. (2012). Time Series Analysis by State Space Methods (2nd ed.). Oxford University Press. ISBN: 978-0199641178