方法对比

	稳健近似贝叶斯计算	鲁棒变分推断
领域	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods
起源年份	2016	2008-2018
提出者	Ruli, Sartori & Ventura; Frazier, Drovandi & Nott (2016–2020)	Fujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018)
类型	likelihood-free inference	Robust approximate Bayesian inference
开创性文献	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.
别名	Robust ABC, robust ABC inference, outlier-robust ABC, robust likelihood-free inference	RVI, robust VI, outlier-robust variational Bayes, power-divergence variational inference
相关	6	6
摘要	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.