เปรียบเทียบวิธี

	Markov Chain Monte Carlo (MCMC)	การจำลองแบบมอนติคาร์โล
สาขาวิชา	การจำลอง	การตัดสินใจ
ตระกูล	Process / pipeline	MCDM
ปีกำเนิด	1953 (Metropolis-Hastings); 1984 (Gibbs)	1949
ผู้ริเริ่ม	Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984)	Metropolis, N., Ulam, S.
ประเภท	Simulation-based Bayesian inference / numerical integration	Robustness wrapper — Monte Carlo uncertainty propagation
แหล่งต้นตำรับ	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.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
ชื่อเรียกอื่น	MCMC, Metropolis-Hastings, Gibbs sampling, Markov Zinciri Monte Carlo (MCMC — Metropolis-Hastings, Gibbs)	—
ที่เกี่ยวข้อง	5	0
สรุป	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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.