Porovnat metody

	Robustní přibližná Bayesovská výpočetní metoda	Robustní bayesovská inference
Obor	Bayesovská statistika	Bayesovská statistika
Rodina	Bayesian methods	Bayesian methods
Rok vzniku	2016	1984–1990
Tvůrce	Ruli, Sartori & Ventura; Frazier, Drovandi & Nott (2016–2020)	James O. Berger
Typ	likelihood-free inference	Bayesian sensitivity / robustness framework
Původní zdroj	Ruli, E., Sartori, N. & Ventura, L. (2016). Approximate Bayesian computation with composite score functions. Statistics and Computing, 26(3), 679–692.	Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328.
Další názvy	Robust ABC, robust ABC inference, outlier-robust ABC, robust likelihood-free inference	Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes
Příbuzné	6	6
Shrnutí	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.	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.