手法を比較

	空間モンテカルロシミュレーション	逐次モンテカルロ法
分野	ベイズ	ベイズ
系統	Bayesian methods	Bayesian methods
提唱年	1970s–1980s	1993 (particle filter); 2006 (SMC samplers)
提唱者	B. D. Ripley and the spatial statistics tradition	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
種類	computational simulation	Sequential Bayesian computation
原典	Ripley, B. D. (1987). Stochastic Simulation. John Wiley & Sons. ISBN: 978-0471818847	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	spatial MC simulation, Monte Carlo spatial analysis, stochastic spatial simulation, spatial stochastic simulation	SMC, particle filter, sequential importance resampling, SMC sampler
関連	4	6
概要	Spatial Monte Carlo simulation applies random sampling methods to spatial problems, generating many stochastic realisations of a spatial process — such as a random field, point pattern, or network — to estimate distributional properties, propagate uncertainty, or test spatial hypotheses. It is a cornerstone technique in geostatistics, spatial epidemiology, ecology, and environmental modelling.	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.