Сравнение методов

	Робастное байесовское усреднение моделей	Робастное байесовское оценивание
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1999–2012	1984–1990
Автор метода	Hoeting, Madigan, Raftery, Volinsky (BMA); robustness extensions by Ley & Steel and others	James O. Berger
Тип	Bayesian model selection and averaging	Bayesian sensitivity / robustness framework
Основополагающий источник	Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401.	Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328.
Другие названия	robust BMA, outlier-robust BMA, robust model averaging, heavy-tailed BMA	Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes
Связанные	6	6
Сводка	Robust Bayesian model averaging extends standard BMA by replacing sensitive conjugate priors with heavy-tailed or mixture priors (e.g., mixtures of g-priors), and optionally robust likelihoods, so that posterior model probabilities and averaged estimates remain stable when data contain outliers, influential observations, or when the prior on model parameters would otherwise dominate the results.	Robust Bayesian inference extends standard Bayesian analysis by replacing a single prior distribution with a class of plausible priors and examining how much the posterior conclusions change across that class. Instead of committing to one prior, the analyst bounds the posterior quantity of interest, revealing whether findings are stable or critically dependent on prior assumptions.