方法对比

	鲁棒哈密顿蒙特卡洛	Gibbs Sampling
领域	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods
起源年份	2010s–2020s	1984
提出者	Livingstone, Zanella and related researchers building on Duane et al. (1987)	Stuart Geman & Donald Geman
类型	Robust MCMC sampler	MCMC sampling algorithm
开创性文献	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.	Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721-741.
别名	Robust HMC, heavy-tailed HMC, geometric-ergodic HMC, outlier-robust HMC	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling
相关	4	5
摘要	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.	Gibbs sampling is a Markov chain Monte Carlo algorithm that approximates a high-dimensional posterior distribution by repeatedly drawing each parameter from its full conditional distribution given all other parameters and the data. Because each draw is exact from a conditional — not a proposal that may be rejected — the sampler is efficient when those conditionals are available in closed form.