Σύγκριση μεθόδων

	Δυναμική Προσομοίωση Monte Carlo	Δυναμική Μπεϋζιανή Συμπερασματολογία
Πεδίο	Μπεϋζιανή Στατιστική	Μπεϋζιανή Στατιστική
Οικογένεια	Bayesian methods	Bayesian methods
Έτος προέλευσης	1975–1977	1989–1997
Δημιουργός	Bortz, Kalos & Lebowitz (physics); Gillespie (chemistry)	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)
Τύπος	stochastic simulation	Bayesian sequential / online inference framework
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	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
Συναφείς	6	6
Σύνοψη	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.