Usporedite metode

	Dinamička Monte Carlo simulacija	Gibbs uzorkovanje
Područje	Bayesovska statistika	Bayesovska statistika
Obitelj	Bayesian methods	Bayesian methods
Godina nastanka	1975–1977	1984
Tvorac	Bortz, Kalos & Lebowitz (physics); Gillespie (chemistry)	Stuart Geman & Donald Geman
Vrsta	stochastic simulation	MCMC sampling algorithm
Temeljni izvor	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.	Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721-741.
Drugi nazivi	DMC simulation, kinetic Monte Carlo, time-driven Monte Carlo, event-driven Monte Carlo	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling
Srodne	6	5
Sažetak	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.	Gibbs sampling is a Markov chain Monte Carlo algorithm that approximates a high-dimensional posterior distribution by repeatedly drawing each parameter from its full conditional distribution given all other parameters and the data. Because each draw is exact from a conditional — not a proposal that may be rejected — the sampler is efficient when those conditionals are available in closed form.