Comparar métodos

	Computação Bayesiana Aproximada com Erro de Medição	MCMC com Erro de Medida
Área	Bayesiano	Bayesiano
Família	Bayesian methods	Bayesian methods
Ano de origem	2013 (measurement-error extension); ABC: 1997-2002	1993
Autor original	Wilkinson, R. D. (formal treatment); ABC roots: Tavaré, Diggle, Beaumont et al. (1997-2002)	Richardson & Gilks; Carroll, Ruppert & Stefanski
Tipo	likelihood-free Bayesian inference	Bayesian computational estimation
Fonte seminal	Wilkinson, R. D. (2013). Approximate Bayesian computation (ABC) gives exact results under the assumption of model error. Statistical Applications in Genetics and Molecular Biology, 12(2), 129-141.	Carroll, R. J., Ruppert, D., Stefanski, L. A. & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Chapman & Hall/CRC. ISBN: 978-1584886334
Outros nomes	ABC with measurement error, ABC-ME, likelihood-free inference with measurement error, simulation-based inference under measurement error	MCMC errors-in-variables, Bayesian measurement error MCMC, MCMC misclassification model, Bayesian errors-in-variables
Relacionados	5	6
Resumo	Approximate Bayesian Computation with measurement error (ABC-ME) extends the standard ABC likelihood-free framework to settings where observed data are themselves noisy or imprecisely recorded. By explicitly incorporating a measurement-error kernel into the acceptance step, ABC-ME targets the correct posterior over model parameters even when the true data-generating process cannot be directly observed.	MCMC with measurement error applies Markov chain Monte Carlo sampling to Bayesian models that explicitly account for the fact that covariates or outcomes are observed with error. By treating the true, unobserved values as latent variables and sampling their joint posterior alongside all other parameters, the method corrects for attenuation bias and produces valid inference even when some variables cannot be measured exactly.