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	Approximate Bayesian Computation per Serie Temporali	Inferenza Bayesiana su Serie Storiche
Campo	Bayesiano	Bayesiano
Famiglia	Bayesian methods	Bayesian methods
Anno di origine	2009	1989
Ideatore	Beaumont, Zhang & Balding (2002) for ABC; Toni et al. (2009) for dynamical/time-series extension	Mike West and Jeff Harrison
Tipo	likelihood-free Bayesian inference	Bayesian probabilistic model
Fonte seminale	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
Alias	TS-ABC, time series ABC, likelihood-free inference for time series, ABC for dynamical systems	Bayesian time series analysis, Bayesian state-space modeling, probabilistic time series inference, BSTS
Correlati	6	6
Sintesi	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.	Time series Bayesian inference applies Bayes' theorem sequentially to time-ordered observations, maintaining a full probability distribution over hidden states and model parameters at every time step. This framework unifies state-space models, dynamic linear models, and particle filters, producing calibrated uncertainty for both filtering (real-time) and retrospective smoothing tasks.