Compară metode

	Filtrul Kalman de Ansamblu	Filtrul particulelor (Monte Carlo secvențial)
Domeniu	Fuziunea datelor	Bayesian
Familie	Regression model	Bayesian methods
Anul apariției	1994	1993
Autorul original	Geir Evensen	Gordon, Salmond & Smith
Tip	Sequential Monte Carlo data assimilation filter	Sequential Monte Carlo estimator
Sursa seminală	Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5), 10143–10162.	Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107–113.
Denumiri alternative	EnKF, Monte Carlo Kalman Filter, Stochastic Ensemble Filter, Topluluk Kalman Filtresi	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Înrudite	3	4
Rezumat	The Ensemble Kalman Filter (EnKF) is a sequential Monte Carlo data assimilation algorithm introduced by Geir Evensen in 1994. It extends the classical Kalman filter to high-dimensional, nonlinear dynamical systems by representing the forecast error covariance through a finite ensemble of model realizations rather than propagating a full covariance matrix. Each ensemble member evolves through the nonlinear model, and observations are assimilated by computing a sample-based Kalman gain, making the method computationally tractable for large geophysical models.	The particle filter, introduced by Gordon, Salmond, and Smith in 1993, is a sequential Monte Carlo algorithm that approximates the Bayesian filtering distribution for nonlinear and non-Gaussian state-space models. Rather than tracking a single best estimate, it maintains a cloud of N weighted random samples — particles — that collectively represent the full posterior distribution of a hidden state at each point in time as new observations arrive.