Σύγκριση μεθόδων

	Προσομοίωση Bootstrap	Προσομοίωση Monte Carlo
Πεδίο	Προσομοίωση	Λήψη Αποφάσεων
Οικογένεια	Process / pipeline	MCDM
Έτος προέλευσης	1979	1949
Δημιουργός	Bradley Efron	Metropolis, N., Ulam, S.
Τύπος	Simulation-based nonparametric inference	Robustness wrapper — Monte Carlo uncertainty propagation
Θεμελιώδης πηγή	Efron, B. & Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Εναλλακτικές ονομασίες	bootstrap resampling, empirical resampling, nonparametric bootstrap, Önyükleme Simülasyonu (Bootstrap Resampling)	—
Συναφείς	5	0
Σύνοψη	Bootstrap simulation, introduced by Bradley Efron in 1979, is a simulation-based inference method that derives the sampling distribution of virtually any statistic by repeatedly resampling with replacement from the observed data. Because it requires no parametric distributional assumptions, it provides a robust, general-purpose alternative to analytical confidence intervals and parametric hypothesis tests across continuous, ordinal, binary, and count data.	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.