Gillespie algorithm
Method for stochastic equation systems
In probability theory and computational science, the Gillespie algorithm, also known as the stochastic simulation algorithm (SSA), is a method for generating statistically exact sample trajectories of certain continuous-time Markov jump processes. It is especially associated with the simulation of coupled chemical reaction systems.
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Gillespie algorithm
Method for stochastic equation systems
In probability theory and computational science, the Gillespie algorithm, also known as the stochastic simulation algorithm (SSA), is a method for generating statistically exact sample trajectories of certain continuous-time Markov jump processes. It is especially associated with the simulation of coupled chemical reaction systems.
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Texto em inglês Ainda não há artigo no seu idioma: trecho em inglês.
In probability theory and computational science, the Gillespie algorithm, also known as the stochastic simulation algorithm (SSA), is a method for generating statistically exact sample trajectories of certain continuous-time Markov jump processes. It is especially associated with the simulation of coupled chemical reaction systems. Daniel Gillespie presented the method in 1976 and developed it further in 1977. The method is widely used in computational systems biology, particularly when the numbers of reacting molecules are small enough that stochastic fluctuations are important. Gillespie-type algorithms are also used in network epidemiology to simulate stochastic epidemic spreading on complex networks, including continuous-time SI, SIS, and SIR processes. Mathematically, the algorithm is a form of dynamic Monte Carlo method and is closely related to kinetic Monte Carlo methods.
Texto: Wikipédia em inglês, CC BY-SA 4.0. ·
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