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	Solidne wnioskowanie bayesowskie	Przybliżone Obliczenia Bayesa
Dziedzina	Statystyka bayesowska	Symulacja
Rodzina	Bayesian methods	Process / pipeline
Rok powstania	1984–1990	2002
Twórca	James O. Berger	—
Typ	Bayesian sensitivity / robustness framework	Simulation-based Bayesian inference
Źródło pierwotne	Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328.	Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.
Inne nazwy	Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes	ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC)
Pokrewne	6	5
Podsumowanie	Robust Bayesian inference extends standard Bayesian analysis by replacing a single prior distribution with a class of plausible priors and examining how much the posterior conclusions change across that class. Instead of committing to one prior, the analyst bounds the posterior quantity of interest, revealing whether findings are stable or critically dependent on prior assumptions.	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.