手法を比較

	尤度フリー推論のための近似ベイズ計算	逐次モンテカルロ法
分野	シミュレーション	ベイズ
系統	Process / pipeline	Bayesian methods
提唱年	2002	1993 (particle filter); 2006 (SMC samplers)
提唱者	—	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
種類	Simulation-based Bayesian inference	Sequential Bayesian computation
原典	Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.	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.
別名	ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC)	SMC, particle filter, sequential importance resampling, SMC sampler
関連	5	6
概要	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.	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.