方法对比

	空间近似贝叶斯计算	空间马尔可夫链蒙特卡洛 (Spatial MCMC)
领域	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods
起源年份	2002 (spatial extensions from mid-2000s)	1990s
提出者	Diggle & Gratton (implicit statistical models, 1984); Beaumont, Zhang & Balding (ABC formalization, 2002)	Gelfand, Smith, and colleagues (early 1990s MCMC for spatial models)
类型	likelihood-free Bayesian inference	Bayesian computational method
开创性文献	Beaumont, M. A., Zhang, W., & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035.	Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173
别名	Spatial ABC, ABC for spatial data, likelihood-free Bayesian spatial inference, simulation-based spatial inference	spatial Markov chain Monte Carlo, MCMC for spatial data, spatial Bayesian MCMC, geostatistical MCMC
相关	4	4
摘要	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.	Spatial MCMC applies Markov chain Monte Carlo sampling to Bayesian models that explicitly account for spatial dependence among observations. It draws posterior samples from models such as conditional autoregressive (CAR), simultaneous autoregressive (SAR), or geostatistical (Gaussian process) models, yielding full uncertainty distributions for spatially structured parameters like random effects, regression coefficients, and spatial range.