Comparer des méthodes

	Modèle bayésien hiérarchique de séries temporelles	MCMC pour séries temporelles
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1989–1997	1994–1997
Auteur d'origine	West & Harrison (dynamic models); Gelman et al. (hierarchical Bayesian framework)	Carter & Kohn; West & Harrison
Type	Bayesian hierarchical model for time series	Bayesian posterior sampling for time-ordered data
Source fondatrice	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259	Carter, C. K. & Kohn, R. (1994). On Gibbs sampling for state space models. Biometrika, 81(3), 541–553.
Alias	TSBHM, Bayesian hierarchical time series, hierarchical dynamic Bayesian model, multilevel Bayesian time series	MCMC time series, Bayesian time series MCMC, time series posterior sampling, sequential Bayesian MCMC
Apparentées	6	6
Résumé	A time series Bayesian hierarchical model combines the hierarchical (multilevel) Bayesian framework with a dynamic state-space structure to analyse temporal data collected on multiple units or groups. Priors encode beliefs about both within-unit dynamics and cross-unit variation, and the posterior is obtained via MCMC or sequential Monte Carlo, yielding full probabilistic forecasts with calibrated uncertainty.	Time series MCMC applies Markov chain Monte Carlo methods to Bayesian inference over time-ordered data. Rather than optimising a single parameter estimate, it draws samples from the full joint posterior of parameters and latent states, yielding probability distributions that honestly reflect uncertainty about dynamics, trends, and seasonal patterns across every time point.