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| 동적 몬테카를로 시뮬레이션× | 순차 몬테카를로 (Sequential Monte Carlo, SMC)× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 1975–1977 | 1993 (particle filter); 2006 (SMC samplers) |
| 창시자≠ | Bortz, Kalos & Lebowitz (physics); Gillespie (chemistry) | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| 유형≠ | stochastic simulation | Sequential Bayesian computation |
| 원전≠ | 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 ↗ | Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113. DOI ↗ |
| 별칭 | DMC simulation, kinetic Monte Carlo, time-driven Monte Carlo, event-driven Monte Carlo | SMC, particle filter, sequential importance resampling, SMC sampler |
| 관련 | 6 | 6 |
| 요약≠ | 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. | Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions. |
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