Sammenlign metoder

	Dynamisk Monte Carlo-simulering	Markov Chain Monte Carlo (MCMC)
Fagområde	Bayesiansk	Simulering
Familie	Bayesian methods	Process / pipeline
Oprindelsesår	1975–1977	1953 (Metropolis-Hastings); 1984 (Gibbs)
Ophavsperson	Bortz, Kalos & Lebowitz (physics); Gillespie (chemistry)	Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984)
Type	stochastic simulation	Simulation-based Bayesian inference / numerical integration
Oprindelig kilde	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.	Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. & Rubin, D.B. (2013). Bayesian Data Analysis (3rd ed.). Chapman & Hall/CRC.
Aliasser	DMC simulation, kinetic Monte Carlo, time-driven Monte Carlo, event-driven Monte Carlo	MCMC, Metropolis-Hastings, Gibbs sampling, Markov Zinciri Monte Carlo (MCMC — Metropolis-Hastings, Gibbs)
Relaterede	6	5
Resumé	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.	Markov Chain Monte Carlo (MCMC) is a family of simulation algorithms that constructs a Markov chain whose stationary distribution is the target posterior, enabling Bayesian inference and high-dimensional integral computation that would otherwise be analytically intractable. Pioneered by Metropolis and colleagues in 1953 and extended by Hastings in 1970, MCMC underpins modern Bayesian statistics. The two most widely used variants are Metropolis-Hastings, which proposes moves from a general proposal distribution, and Gibbs sampling, which draws each parameter in turn from its full conditional distribution.