Comparar métodos

	Computação Bayesiana Aproximada Espacial	MCMC Espacial
Área	Bayesiano	Bayesiano
Família	Bayesian methods	Bayesian methods
Ano de origem	2002 (spatial extensions from mid-2000s)	1990s
Autor original	Diggle & Gratton (implicit statistical models, 1984); Beaumont, Zhang & Balding (ABC formalization, 2002)	Gelfand, Smith, and colleagues (early 1990s MCMC for spatial models)
Tipo	likelihood-free Bayesian inference	Bayesian computational method
Fonte seminal	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
Outros nomes	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
Relacionados	4	4
Resumo	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.