Compara mètodes

	Computació Bayesiana Aproximada de Sèries Temporals	Inferència Bayesiana Dinàmica
Camp	Bayesià	Bayesià
Família	Bayesian methods	Bayesian methods
Any d'origen	2009	1989–1997
Autor original	Beaumont, Zhang & Balding (2002) for ABC; Toni et al. (2009) for dynamical/time-series extension	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)
Tipus	likelihood-free Bayesian inference	Bayesian sequential / online inference framework
Font seminal	Toni, T., Welch, D., Strelkowa, N., Ipsen, A. & Stumpf, M. P. H. (2009). Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of the Royal Society Interface, 6(31), 187–202.	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
Àlies	TS-ABC, time series ABC, likelihood-free inference for time series, ABC for dynamical systems	online Bayesian inference, sequential Bayesian updating, recursive Bayesian estimation, dynamic Bayesian updating
Relacionats	6	6
Resum	Time series ABC is a likelihood-free Bayesian inference method that estimates the posterior distribution of model parameters for dynamical or time-indexed systems by comparing summary statistics of simulated trajectories to those of the observed series, bypassing the need to evaluate an analytic likelihood. It is particularly valuable for complex mechanistic or stochastic models whose likelihoods are intractable.	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.