Porovnat metody

	Robustní variační inference	Aproximované bayesovské počty
Obor	Bayesovská statistika	Simulace
Rodina	Bayesian methods	Process / pipeline
Rok vzniku	2008-2018	2002
Tvůrce	Fujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018)	—
Typ	Robust approximate Bayesian inference	Simulation-based Bayesian inference
Původní zdroj	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.	Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.
Další názvy	RVI, robust VI, outlier-robust variational Bayes, power-divergence variational inference	ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC)
Příbuzné	6	5
Shrnutí	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.	Approximate Bayesian Computation (ABC) is a family of simulation-based inference methods that estimate posterior distributions without requiring an analytically tractable likelihood function. Introduced by Beaumont, Zhang and Balding (2002) in the context of population genetics, ABC replaced the intractable likelihood with repeated model simulation and a comparison of summary statistics between simulated and observed data.