Comparer des méthodes

	Simulation de Monte-Carlo dynamique	Inférence bayésienne dynamique
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1975–1977	1989–1997
Auteur d'origine	Bortz, Kalos & Lebowitz (physics); Gillespie (chemistry)	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)
Type	stochastic simulation	Bayesian sequential / online inference framework
Source fondatrice	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
Alias	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
Apparentées	6	6
Résumé	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.