Сравнение на методи

	Устойчив Хамилтонов Монте Карло	Robust Bayesian Inference
Област	Бейсови методи	Бейсови методи
Семейство	Bayesian methods	Bayesian methods
Година на възникване	2010s–2020s	1984–1990
Създател	Livingstone, Zanella and related researchers building on Duane et al. (1987)	James O. Berger
Тип	Robust MCMC sampler	Bayesian sensitivity / robustness framework
Основополагащ източник	Livingstone, S. & Zanella, G. (2022). The Barker proposal: combining robustness and efficiency in gradient-based MCMC. Journal of the Royal Statistical Society: Series B, 84(2), 496–523.	Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328.
Други названия	Robust HMC, heavy-tailed HMC, geometric-ergodic HMC, outlier-robust HMC	Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes
Свързани	4	6
Резюме	Robust Hamiltonian Monte Carlo (Robust HMC) is a family of extensions to standard HMC designed to maintain geometric ergodicity and sampling efficiency when the posterior has heavy tails, strong curvature variation, or near-degenerate geometry. By modifying the kinetic energy, mass matrix, or proposal mechanism, these methods ensure reliable exploration of difficult posteriors that defeat the standard NUTS/HMC sampler.	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.