Σύγκριση μεθόδων

	Δυναμικό Φίλτρο Σωματιδίων	Φίλτρο Kalman
Πεδίο	Μπεϋζιανή Στατιστική	Μπεϋζιανή Στατιστική
Οικογένεια	Bayesian methods	Bayesian methods
Έτος προέλευσης	1993	1960
Δημιουργός	Gordon, Salmond & Smith (bootstrap particle filter, 1993); extended by Doucet et al. (2001)	Rudolf E. Kalman
Τύπος	Sequential Bayesian state estimation	recursive Bayesian filter
Θεμελιώδης πηγή	Doucet, A., de Freitas, N. & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer. ISBN: 978-0387951461	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.
Εναλλακτικές ονομασίες	dynamic sequential Monte Carlo, dynamic SMC, bootstrap particle filter, dynamic SIR filter	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
Συναφείς	4	5
Σύνοψη	A dynamic particle filter is a sequential Monte Carlo algorithm that tracks an evolving hidden state over time by maintaining a population of weighted random samples — particles — each representing a plausible trajectory. As new observations arrive, particle weights are updated via the likelihood and the population is resampled, keeping the representation concentrated on the most probable state regions in a fully nonlinear and non-Gaussian setting.	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.