Comparer des méthodes

	Réseau bayésien dynamique	Monte Carlo séquentiel
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1989	1993 (particle filter); 2006 (SMC samplers)
Auteur d'origine	Thomas Dean & Keiji Kanazawa	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Type	probabilistic graphical model for sequences	Sequential Bayesian computation
Source fondatrice	Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.	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	DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network	SMC, particle filter, sequential importance resampling, SMC sampler
Apparentées	5	6
Résumé	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.	Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions.