So sánh phương pháp

	Mô phỏng Monte Carlo Động	Suy luận Bayes động
Lĩnh vực	Bayes	Bayes
Họ	Bayesian methods	Bayesian methods
Năm ra đời	1975–1977	1989–1997
Người khởi xướng	Bortz, Kalos & Lebowitz (physics); Gillespie (chemistry)	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)
Loại	stochastic simulation	Bayesian sequential / online inference framework
Công trình gốc	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.	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
Tên gọi khác	DMC simulation, kinetic Monte Carlo, time-driven Monte Carlo, event-driven Monte Carlo	online Bayesian inference, sequential Bayesian updating, recursive Bayesian estimation, dynamic Bayesian updating
Liên quan	6	6
Tóm tắt	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.	Dynamic Bayesian inference is a framework for performing Bayesian updating sequentially as new observations arrive over time. Rather than fitting a static model to a fixed dataset, it tracks how a posterior distribution over latent states or parameters evolves step by step, combining a prior with each new likelihood to produce an updated posterior that propagates forward through time.