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	Robust Approximate Bayesian Computation	Bayesiansk inferens med mätfel
Ämnesområde	Bayesiansk statistik	Bayesiansk statistik
Familj	Bayesian methods	Bayesian methods
Ursprungsår	2016	1993
Upphovsperson	Ruli, Sartori & Ventura; Frazier, Drovandi & Nott (2016–2020)	Richardson & Gilks (Bayesian formulation); Carroll et al. (comprehensive framework)
Typ	likelihood-free inference	Bayesian errors-in-variables model
Ursprungskälla	Ruli, E., Sartori, N. & Ventura, L. (2016). Approximate Bayesian computation with composite score functions. Statistics and Computing, 26(3), 679–692.	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: 978-1584886433
Alias	Robust ABC, robust ABC inference, outlier-robust ABC, robust likelihood-free inference	Bayesian errors-in-variables model, Bayesian EIV model, Bayesian measurement error model, Bayesian misclassification model
Närliggande	6	5
Sammanfattning	Robust ABC extends standard Approximate Bayesian Computation to handle outliers, model misspecification, and sensitivity to summary statistic choice. By replacing conventional distance measures with robust alternatives — such as composite scores, trimmed statistics, or synthetic likelihoods — it protects posterior inference from being distorted by atypical observations or an imperfect simulator.	Bayesian inference with measurement error extends the standard Bayesian framework to situations where one or more covariates or outcomes are observed with noise or misclassification. By treating the true unobserved values as latent variables and assigning them priors, the model jointly estimates the true exposure distribution and the structural parameters of interest, propagating all uncertainty through the posterior.