Vertaile menetelmiä
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| Dynaaminen Monte Carlo -simulaatio× | Bootstrap-simulaatio – empiirinen uudelleennäytteistys tilastolliseen päättelyyn× | |
|---|---|---|
| Tieteenala≠ | Bayesilainen tilastotiede | Simulointi |
| Menetelmäperhe≠ | Bayesian methods | Process / pipeline |
| Syntyvuosi≠ | 1975–1977 | 1979 |
| Kehittäjä≠ | Bortz, Kalos & Lebowitz (physics); Gillespie (chemistry) | Bradley Efron |
| Tyyppi≠ | stochastic simulation | Simulation-based nonparametric inference |
| Alkuperäislähde≠ | Bortz, A. B., Kalos, M. H., & Lebowitz, J. L. (1975). A new algorithm for Monte Carlo simulation of Ising spin systems. Journal of Computational Physics, 17(1), 10–18. DOI ↗ | Efron, B. & Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC. DOI ↗ |
| Rinnakkaisnimet | DMC simulation, kinetic Monte Carlo, time-driven Monte Carlo, event-driven Monte Carlo | bootstrap resampling, empirical resampling, nonparametric bootstrap, Önyükleme Simülasyonu (Bootstrap Resampling) |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | Dynamic Monte Carlo (DMC) simulation is a computational method that tracks the stochastic time evolution of a system by drawing random event sequences weighted by transition rates. Unlike static Monte Carlo sampling of equilibrium distributions, DMC explicitly advances a clock, making it suitable for kinetic, reaction, and time-dependent phenomena where the sequence and timing of events matter. | 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. |
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