Compara mètodes

	Còmputació Bayesiana Aproximada Espacial	Monte Carlo Seqüencial
Camp	Bayesià	Bayesià
Família	Bayesian methods	Bayesian methods
Any d'origen	2002 (spatial extensions from mid-2000s)	1993 (particle filter); 2006 (SMC samplers)
Autor original	Diggle & Gratton (implicit statistical models, 1984); Beaumont, Zhang & Balding (ABC formalization, 2002)	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Tipus	likelihood-free Bayesian inference	Sequential Bayesian computation
Font seminal	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.
Àlies	Spatial ABC, ABC for spatial data, likelihood-free Bayesian spatial inference, simulation-based spatial inference	SMC, particle filter, sequential importance resampling, SMC sampler
Relacionats	4	6
Resum	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.