방법 비교

	다단계 해밀턴ian 몬테카를로	Markov Chain Monte Carlo (MCMC)
분야	베이지안	시뮬레이션
계열	Bayesian methods	Process / pipeline
기원 연도	2010s	1953 (Metropolis-Hastings); 1984 (Gibbs)
창시자	Beskos, Jasra, Law, Tempone, Zhou (multilevel MCMC); Neal (HMC component)	Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984)
유형	Bayesian computational sampler	Simulation-based Bayesian inference / numerical integration
원전	Beskos, A., Jasra, A., Law, K., Tempone, R., & Zhou, Y. (2017). Multilevel sequential Monte Carlo samplers. Stochastic Processes and their Applications, 127(5), 1417–1440.	Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. & Rubin, D.B. (2013). Bayesian Data Analysis (3rd ed.). Chapman & Hall/CRC.
별칭	Multilevel HMC, MLHMC, multilevel HMC sampler, multilevel leapfrog MCMC	MCMC, Metropolis-Hastings, Gibbs sampling, Markov Zinciri Monte Carlo (MCMC — Metropolis-Hastings, Gibbs)
관련	5	5
요약	Multilevel Hamiltonian Monte Carlo (Multilevel HMC) combines the variance-reduction strategy of multilevel Monte Carlo with the efficient gradient-driven exploration of Hamiltonian Monte Carlo. By running coupled HMC chains at increasing levels of model fidelity or discretisation, it achieves accurate posterior estimates at a computational cost substantially lower than a single fine-level HMC chain.	Markov Chain Monte Carlo (MCMC) is a family of simulation algorithms that constructs a Markov chain whose stationary distribution is the target posterior, enabling Bayesian inference and high-dimensional integral computation that would otherwise be analytically intractable. Pioneered by Metropolis and colleagues in 1953 and extended by Hastings in 1970, MCMC underpins modern Bayesian statistics. The two most widely used variants are Metropolis-Hastings, which proposes moves from a general proposal distribution, and Gibbs sampling, which draws each parameter in turn from its full conditional distribution.