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Monte Carlo Acceptance | Open Access Journals
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Journal of Applied & Computational Mathematics

ISSN: 2168-9679

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Monte Carlo Acceptance

Monte Carlo methods, or Monte Carlo experiments, are a large class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems which could in principle be deterministic. They are often used in physical and mathematical issues, and are most useful when using other approaches is difficult or impossible. The methods of Monte Carlo are used in three classes of problems optimization, numerical integration, and generating draws from a distribution of probability. In physics related issues, Monte Carlo methods are useful in simulating systems with several degrees of freedom, such as fluids, disordered materials, closely bound solids, and cellular structures. Other examples include modelling phenomena with significant uncertainty in inputs such as business risk calculation and multidimensional assessment of definite integrals with. The Monte Carlo-based predictions of failure, cost overruns and schedule overruns are routinely better than human intuition or alternative "soft" methods in application to system engineering problems.

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Citations: 1282

Journal of Applied & Computational Mathematics received 1282 citations as per Google Scholar report

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