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| Робастна вариационна инференция× | Robust Markov Chain Monte Carlo× | |
|---|---|---|
| Област | Бейсови методи | Бейсови методи |
| Семейство | Bayesian methods | Bayesian methods |
| Година на възникване≠ | 2008-2018 | 2000s–2010s |
| Създател≠ | Fujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018) | Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others |
| Тип≠ | Robust approximate Bayesian inference | Bayesian computational sampling |
| Основополагащ източник≠ | 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. link ↗ | Roberts, G. O. & Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71. DOI ↗ |
| Други названия | RVI, robust VI, outlier-robust variational Bayes, power-divergence variational inference | robust MCMC, outlier-robust MCMC, robust posterior sampling, misspecification-robust MCMC |
| Свързани≠ | 6 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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