Comparar métodos

	MCMC para Séries Temporais	Inferência Bayesiana Dinâmica
Área	Bayesiano	Bayesiano
Família	Bayesian methods	Bayesian methods
Ano de origem	1994–1997	1989–1997
Autor original	Carter & Kohn; West & Harrison	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)
Tipo	Bayesian posterior sampling for time-ordered data	Bayesian sequential / online inference framework
Fonte seminal	Carter, C. K. & Kohn, R. (1994). On Gibbs sampling for state space models. Biometrika, 81(3), 541–553.	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
Outros nomes	MCMC time series, Bayesian time series MCMC, time series posterior sampling, sequential Bayesian MCMC	online Bayesian inference, sequential Bayesian updating, recursive Bayesian estimation, dynamic Bayesian updating
Relacionados	6	6
Resumo	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.	Dynamic Bayesian inference is a framework for performing Bayesian updating sequentially as new observations arrive over time. Rather than fitting a static model to a fixed dataset, it tracks how a posterior distribution over latent states or parameters evolves step by step, combining a prior with each new likelihood to produce an updated posterior that propagates forward through time.