Comparar métodos

	Computação Bayesiana Aproximada para Séries Temporais	Monte Carlo Sequencial
Área	Bayesiano	Bayesiano
Família	Bayesian methods	Bayesian methods
Ano de origem	2009	1993 (particle filter); 2006 (SMC samplers)
Autor original	Beaumont, Zhang & Balding (2002) for ABC; Toni et al. (2009) for dynamical/time-series extension	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Tipo	likelihood-free Bayesian inference	Sequential Bayesian computation
Fonte 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.	Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113.
Outros nomes	TS-ABC, time series ABC, likelihood-free inference for time series, ABC for dynamical systems	SMC, particle filter, sequential importance resampling, SMC sampler
Relacionados	6	6
Resumo	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.	Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions.