Vertaile menetelmiä

	Dynaaminen Monte Carlo -simulaatio	Sekventiaalinen Monte Carlo
Tieteenala	Bayesilainen tilastotiede	Bayesilainen tilastotiede
Menetelmäperhe	Bayesian methods	Bayesian methods
Syntyvuosi	1975–1977	1993 (particle filter); 2006 (SMC samplers)
Kehittäjä	Bortz, Kalos & Lebowitz (physics); Gillespie (chemistry)	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Tyyppi	stochastic simulation	Sequential Bayesian computation
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.	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.
Rinnakkaisnimet	DMC simulation, kinetic Monte Carlo, time-driven Monte Carlo, event-driven Monte Carlo	SMC, particle filter, sequential importance resampling, SMC sampler
Liittyvät	6	6
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.	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.