방법 비교

	강건한 해밀토니안 몬테카를로	해밀토니안 몬테카를로
분야	베이지안	베이지안
계열	Bayesian methods	Bayesian methods
기원 연도	2010s–2020s	1987
창시자	Livingstone, Zanella and related researchers building on Duane et al. (1987)	—
유형	Robust MCMC sampler	Gradient-based Markov chain Monte Carlo sampler
원전	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.	Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters B, 195(2), 216–222.
별칭	Robust HMC, heavy-tailed HMC, geometric-ergodic HMC, outlier-robust HMC	HMC, Hybrid Monte Carlo, NUTS, No-U-Turn Sampler
관련	4	3
요약	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.	Hamiltonian Monte Carlo (HMC) is a gradient-based Markov chain Monte Carlo algorithm that uses the geometry of the log-posterior surface to make large, informed jumps through parameter space instead of the small random steps of classical MCMC. Originally introduced for lattice field theory by Duane, Kennedy, Pendleton, and Roweth (1987) under the name Hybrid Monte Carlo, and brought into mainstream statistics by Radford Neal's authoritative 2011 chapter, HMC is today the default sampler in Stan and PyMC and is widely regarded as the state-of-the-art engine for Bayesian posterior inference in high-dimensional models.