2006Computer and Information TechnologyRequires access

Monte Carlo Simulation Computer and its application

QI Xiao-jun

Open publisher page 2 citations

Abstract

Monte Carlo Simulation engage in computational simulation to the stochastic problems with Monte Carlo method,and find a solution of need resolving questiong.In order to research complicated stochastic problems,In this paper,it put forward Monte Carlo Simulation Computer of based on Monte Carlo stochastic simulated method,and illustrates its fundamental tenets.To the calculation of ration of the circumference of a circle to its diameter,it practices applied process of Monte Carlo Simulation Computer.It shows adtantage and general adaptability of Monte Carlo Simulation Computer for dealing with stochastic problems.

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What this paper is about

Monte Carlo Simulation engage in computational simulation to the stochastic problems with Monte Carlo method,and find a solution of need resolving questiong.In order to research complicated stochastic problems,In this paper,it put forward Monte Carlo Simulation Computer of based on Monte Carlo stochastic simulated method,and illustrates its fundamental tenets.To the calculation of ration of the circumference of a circle to its diameter,it practices applied process of Monte Carlo Simulation Computer.It shows adtantage and general adaptability of Monte Carlo Simulation Computer for dealing with stochastic problems.

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Available abstract

Monte Carlo Simulation engage in computational simulation to the stochastic problems with Monte Carlo method,and find a solution of need resolving questiong.In order to research complicated stochastic problems,In this paper,it put forward Monte Carlo Simulation Computer of based on Monte Carlo stochastic simulated method,and illustrates its fundamental tenets.To the calculation of ration of the circumference of a circle to its diameter,it practices applied process of Monte Carlo Simulation Computer.It shows adtantage and general adaptability of Monte Carlo Simulation Computer for dealing with stochastic problems.

Key concepts: Monte Carlo method, Monte Carlo molecular modeling, Computer science, Quasi-Monte Carlo method, Monte Carlo method in statistical physics, Monte Carlo integration, Dynamic Monte Carlo method, Hybrid Monte Carlo

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