Comparer des méthodes

	Inférence bayésienne sur séries temporelles	Réseau bayésien dynamique
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1989	1989
Auteur d'origine	Mike West and Jeff Harrison	Thomas Dean & Keiji Kanazawa
Type	Bayesian probabilistic model	probabilistic graphical model for sequences
Source fondatrice	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259	Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.
Alias	Bayesian time series analysis, Bayesian state-space modeling, probabilistic time series inference, BSTS	DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network
Apparentées	6	5
Résumé	Time series Bayesian inference applies Bayes' theorem sequentially to time-ordered observations, maintaining a full probability distribution over hidden states and model parameters at every time step. This framework unifies state-space models, dynamic linear models, and particle filters, producing calibrated uncertainty for both filtering (real-time) and retrospective smoothing tasks.	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.