Σύγκριση μεθόδων

	Μέθοδοι Markov Chain Monte Carlo (MCMC)	Προσομοίωση Monte Carlo
Πεδίο	Προσομοίωση	Λήψη Αποφάσεων
Οικογένεια	Process / pipeline	MCDM
Έτος προέλευσης	1953 (Metropolis-Hastings); 1984 (Gibbs)	1949
Δημιουργός	Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984)	Metropolis, N., Ulam, S.
Τύπος	Simulation-based Bayesian inference / numerical integration	Robustness wrapper — Monte Carlo uncertainty propagation
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	MCMC, Metropolis-Hastings, Gibbs sampling, Markov Zinciri Monte Carlo (MCMC — Metropolis-Hastings, Gibbs)	—
Συναφείς	5	0
Σύνοψη	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.