Сравнение методов

	Многоуровневая Монте-Карло симуляция	Марковские цепи Монте-Карло (MCMC)
Область	Байесовские методы	Имитационное моделирование
Семейство	Bayesian methods	Process / pipeline
Год появления	2008	1953 (Metropolis-Hastings); 1984 (Gibbs)
Автор метода	Michael B. Giles	Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984)
Тип	variance-reduction simulation	Simulation-based Bayesian inference / numerical integration
Основополагающий источник	Giles, M. B. (2008). Multilevel Monte Carlo path simulation. Operations Research, 56(3), 607–617.	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.
Другие названия	MLMC, multilevel MC, multi-level Monte Carlo, MLMC simulation	MCMC, Metropolis-Hastings, Gibbs sampling, Markov Zinciri Monte Carlo (MCMC — Metropolis-Hastings, Gibbs)
Связанные	4	5
Сводка	Multilevel Monte Carlo (MLMC) is a variance-reduction technique that estimates expectations by combining simulations run at multiple levels of numerical resolution. Coarse, cheap simulations capture most of the signal; fine, expensive simulations correct only the remaining small difference — dramatically reducing total computational cost compared with standard Monte Carlo at the finest level alone.	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.