Bayesian methodsBayesian / computational
Kalman Filter
The Kalman filter is an optimal recursive algorithm for estimating the hidden state of a linear dynamical system from noisy measurements. At each time step it alternates between a prediction step — projecting the state forward using the system model — and an update step that corrects the prediction with the new observation, producing minimum-variance state estimates and their uncertainty in real time.
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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 ↗
- Welch, G. & Bishop, G. (2006). An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill, Technical Report TR 95-041. link ↗
Related methods
Referenced by
Bayesian Inference with Measurement ErrorDigital Twin SimulationDynamic Bayesian Hierarchical ModelDynamic Bayesian InferenceDynamic Bayesian Model AveragingDynamic Bayesian NetworkDynamic Metropolis-Hastings AlgorithmDynamic Particle FilterDynamic Sequential Monte CarloDynamic Variational InferenceHierarchical Bootstrap SimulationHierarchical Kalman FilterHierarchical Particle FilterKalman Filter with Measurement ErrorKalman Filter with Missing DataLinear Quadratic GaussianMarkov-Switching MultifractalParticle FilterParticle Filter with Measurement ErrorRobust Kalman FilterRobust Particle FilterRobust Sequential Monte CarloSequential Monte CarloSpatial Bootstrap SimulationSpatial Kalman FilterTime series approximate Bayesian computationTime series Bayesian hierarchical modelTime series Bayesian inferenceTime series Bayesian model averagingTime Series Kalman FilterTime series MCMCTime series particle filterTime series sequential Monte CarloTime series variational inferenceTime-varying parameter AR modelTime-varying parameter ARCH modelTime-varying parameter ARIMA modelTime-varying parameter ARMA modelTime-varying parameter Engle-Granger cointegrationTime-varying parameter GARCH modelTime-varying parameter GLSTime-varying parameter Granger causalityTime-varying parameter MA modelTime-varying parameter OLSTime-varying Parameter Panel Data AnalysisTime-varying parameter SARIMA modelTime-varying parameter VAR modelTime-varying parameter VECM