Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Динамична симулация по метода на Монте Карло× | Буутстрап симулация× | |
|---|---|---|
| Област≠ | Бейсови методи | Симулационно моделиране |
| Семейство≠ | Bayesian methods | Process / pipeline |
| Година на възникване≠ | 1975–1977 | 1979 |
| Създател≠ | Bortz, Kalos & Lebowitz (physics); Gillespie (chemistry) | Bradley Efron |
| Тип≠ | stochastic simulation | Simulation-based nonparametric inference |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | DMC simulation, kinetic Monte Carlo, time-driven Monte Carlo, event-driven Monte Carlo | bootstrap resampling, empirical resampling, nonparametric bootstrap, Önyükleme Simülasyonu (Bootstrap Resampling) |
| Свързани≠ | 6 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
|
|