방법 비교

	강건 근사 베이즈 추론 (Robust Approximate Bayesian Computation)	강건 변분 추론
분야	베이지안	베이지안
계열	Bayesian methods	Bayesian methods
기원 연도	2016	2008-2018
창시자	Ruli, Sartori & Ventura; Frazier, Drovandi & Nott (2016–2020)	Fujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018)
유형	likelihood-free inference	Robust approximate Bayesian inference
원전	Ruli, E., Sartori, N. & Ventura, L. (2016). Approximate Bayesian computation with composite score functions. Statistics and Computing, 26(3), 679–692.	Futami, F., Sato, I. & Sugiyama, M. (2018). Variational inference based on robust divergences. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 84:813-822.
별칭	Robust ABC, robust ABC inference, outlier-robust ABC, robust likelihood-free inference	RVI, robust VI, outlier-robust variational Bayes, power-divergence variational inference
관련	6	6
요약	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 variational inference (RVI) extends standard variational inference by replacing the Kullback-Leibler divergence with a divergence measure that is less sensitive to outliers and model misspecification — such as the beta-divergence or a Renyi-type divergence. This yields posterior approximations that remain well-behaved even when a fraction of the data departs from the assumed model.