Сравнение методов

	Последовательный Монте-Карло	Приближенное байесовское вычисление
Область	Байесовские методы	Имитационное моделирование
Семейство	Bayesian methods	Process / pipeline
Год появления	1993 (particle filter); 2006 (SMC samplers)	2002
Автор метода	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)	—
Тип	Sequential Bayesian computation	Simulation-based Bayesian inference
Основополагающий источник	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.	Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.
Другие названия	SMC, particle filter, sequential importance resampling, SMC sampler	ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC)
Связанные	6	5
Сводка	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.	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.