Comparer des méthodes

	Simulation par bootstrap spatial	Filtre de Kalman
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1990s–2000s	1960
Auteur d'origine	Lahiri and others, building on Efron's bootstrap (1979)	Rudolf E. Kalman
Type	Resampling / simulation	recursive Bayesian filter
Source fondatrice	Lahiri, S. N. (2003). Resampling Methods for Dependent Data. Springer. ISBN: 978-0387009285	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.
Alias	spatial block bootstrap, spatial resampling, geostatistical bootstrap, bootstrap for spatial data	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
Apparentées	4	5
Résumé	Spatial bootstrap simulation is a resampling technique designed for spatially dependent data. By resampling contiguous spatial blocks rather than independent observations, it preserves the local autocorrelation structure of the data and yields valid estimates of sampling variability for statistics computed on geographic or lattice observations.	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.