Comparer des méthodes

	Calcul bayésien approximatif pour séries temporelles	Calcul bayésien approximatif
Domaine	Bayésien	Simulation
Famille	Bayesian methods	Process / pipeline
Année d'origine	2009	2002
Auteur d'origine	Beaumont, Zhang & Balding (2002) for ABC; Toni et al. (2009) for dynamical/time-series extension	—
Type	likelihood-free Bayesian inference	Simulation-based Bayesian inference
Source fondatrice	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.	Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.
Alias	TS-ABC, time series ABC, likelihood-free inference for time series, ABC for dynamical systems	ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC)
Apparentées	6	5
Résumé	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.	Approximate Bayesian Computation (ABC) is a family of simulation-based inference methods that estimate posterior distributions without requiring an analytically tractable likelihood function. Introduced by Beaumont, Zhang and Balding (2002) in the context of population genetics, ABC replaced the intractable likelihood with repeated model simulation and a comparison of summary statistics between simulated and observed data.