Comparer des méthodes

	Calcul Bayésien Approché Spatial	Monte Carlo séquentiel
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	2002 (spatial extensions from mid-2000s)	1993 (particle filter); 2006 (SMC samplers)
Auteur d'origine	Diggle & Gratton (implicit statistical models, 1984); Beaumont, Zhang & Balding (ABC formalization, 2002)	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Type	likelihood-free Bayesian inference	Sequential Bayesian computation
Source fondatrice	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.
Alias	Spatial ABC, ABC for spatial data, likelihood-free Bayesian spatial inference, simulation-based spatial inference	SMC, particle filter, sequential importance resampling, SMC sampler
Apparentées	4	6
Résumé	Spatial Approximate Bayesian Computation (Spatial ABC) is a likelihood-free Bayesian inference framework for spatial data models whose likelihood function is intractable or too expensive to evaluate. It draws candidate parameters from a prior, simulates spatially structured datasets under those parameters, and accepts only the draws whose simulated spatial summary statistics closely match the observed data, thereby building an approximate posterior over model parameters.	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.