Comparer des méthodes

	Filtre particulaire dynamique	Filtre particulaire (Monte Carlo séquentiel)
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1993	1993
Auteur d'origine	Gordon, Salmond & Smith (bootstrap particle filter, 1993); extended by Doucet et al. (2001)	Gordon, Salmond & Smith
Type	Sequential Bayesian state estimation	Sequential Monte Carlo estimator
Source fondatrice	Doucet, A., de Freitas, N. & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer. ISBN: 978-0387951461	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.
Alias	dynamic sequential Monte Carlo, dynamic SMC, bootstrap particle filter, dynamic SIR filter	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Apparentées	4	4
Résumé	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 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.