Comparer des méthodes

	Filtrage Séquentiel par Monte-Carlo pour Séries Temporelles	Réseau bayésien dynamique
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1993	1989
Auteur d'origine	Gordon, Salmond & Smith	Thomas Dean & Keiji Kanazawa
Type	Sequential Bayesian filtering algorithm	probabilistic graphical model for sequences
Source fondatrice	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.	Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.
Alias	particle filter, time series SMC, sequential particle filtering, bootstrap particle filter	DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network
Apparentées	5	5
Résumé	Time series sequential Monte Carlo (SMC), commonly called the particle filter, is a Bayesian simulation method that tracks the hidden state of a dynamical system as observations arrive one at a time. A cloud of weighted random samples — particles — is propagated forward through the system dynamics, reweighted by how well each particle explains the new observation, and periodically resampled to keep the representation concentrated on plausible states.	A Dynamic Bayesian Network (DBN) extends a standard Bayesian network over time by representing how a set of random variables evolve across discrete time steps. It captures both the conditional independence structure among variables at each instant and the probabilistic dependencies between consecutive time slices, enabling principled reasoning about temporal processes under uncertainty.