Võrdle meetodeid

	Robustne variatsiooniline järeldus	Robust Markov Chain Monte Carlo
Valdkond	Bayesi meetodid	Bayesi meetodid
Perekond	Bayesian methods	Bayesian methods
Tekkeaasta	2008-2018	2000s–2010s
Looja	Fujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018)	Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others
Tüüp	Robust approximate Bayesian inference	Bayesian computational sampling
Algallikas	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.
Rööpnimetused	RVI, robust VI, outlier-robust variational Bayes, power-divergence variational inference	robust MCMC, outlier-robust MCMC, robust posterior sampling, misspecification-robust MCMC
Seotud	6	5
Kokkuvõte	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.