เปรียบเทียบวิธี

	การอนุมานแบบแปรผันที่ทนทาน (Robust Variational Inference)	Markov Chain Monte Carlo (MCMC) แบบทนทาน
สาขาวิชา	เบย์	เบย์
ตระกูล	Bayesian methods	Bayesian methods
ปีกำเนิด	2008-2018	2000s–2010s
ผู้ริเริ่ม	Fujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018)	Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others
ประเภท	Robust approximate Bayesian inference	Bayesian computational sampling
แหล่งต้นตำรับ	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.	Roberts, G. O. & Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71.
ชื่อเรียกอื่น	RVI, robust VI, outlier-robust variational Bayes, power-divergence variational inference	robust MCMC, outlier-robust MCMC, robust posterior sampling, misspecification-robust MCMC
ที่เกี่ยวข้อง	6	5
สรุป	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.	Robust MCMC combines Markov chain Monte Carlo sampling with robustness techniques to produce reliable posterior inference when data contain outliers, when the assumed model is misspecified, or when the target distribution has heavy tails that cause standard samplers to mix poorly or yield distorted estimates.