Methoden vergleichen

	Approximate Bayesian Computation mit Messfehlern	Approximate Bayesian Computation
Fachgebiet	Bayes-Statistik	Simulation
Familie	Bayesian methods	Process / pipeline
Entstehungsjahr	2013 (measurement-error extension); ABC: 1997-2002	2002
Urheber	Wilkinson, R. D. (formal treatment); ABC roots: Tavaré, Diggle, Beaumont et al. (1997-2002)	—
Typ	likelihood-free Bayesian inference	Simulation-based Bayesian inference
Wegweisende Quelle	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.	Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.
Aliasnamen	ABC with measurement error, ABC-ME, likelihood-free inference with measurement error, simulation-based inference under measurement error	ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC)
Verwandt	5	5
Zusammenfassung	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.	Approximate Bayesian Computation (ABC) is a family of simulation-based inference methods that estimate posterior distributions without requiring an analytically tractable likelihood function. Introduced by Beaumont, Zhang and Balding (2002) in the context of population genetics, ABC replaced the intractable likelihood with repeated model simulation and a comparison of summary statistics between simulated and observed data.