Porovnat metody

	Bayesovský hierarchický model časových řad	MCMC pro časové řady
Obor	Bayesovská statistika	Bayesovská statistika
Rodina	Bayesian methods	Bayesian methods
Rok vzniku	1989–1997	1994–1997
Tvůrce	West & Harrison (dynamic models); Gelman et al. (hierarchical Bayesian framework)	Carter & Kohn; West & Harrison
Typ	Bayesian hierarchical model for time series	Bayesian posterior sampling for time-ordered data
Původní zdroj	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.
Další názvy	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
Příbuzné	6	6
Shrnutí	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.