“Monte Carlo methods”的概念、定义、翻译、参考文献-科学参考

Monte Carlo methods

Mathematics

The approximate solution of a problem using repeated sampling experiments and observing the proportion of times some property is satisfied. Estimates of probabilities may be computed by running a simulation repeatedly and using the relative frequency.

Statistics

Methods that use pseudo-random numbers in order to determine the properties of some function or set of functions. For example, the acceptance–rejection algorithm can be used to evaluate integrals, simulation makes use of pseudo-random numbers to study the outcome of some complex system, and Monte Carlo tests use pseudo-random numbers to decide on the outcome of hypothesis tests. The accuracy of the methods generally improves with an increase in the number of pseudo-random numbers used.

Computer

Numerical methods in which randomly generated numbers play a part in the calculations. A probabilistic model is constructed, corresponding to the mathematical or physical problem, and random samples are taken within the model. By taking more samples, a more accurate estimate of the result is obtained. Such methods are used for example on problems in particle physics, evaluation of multiple integrals, traffic problems, and large-scale operational problems generally. See also stochastic process.

单词	Monte Carlo methods
释义	Monte Carlo methods Mathematics The approximate solution of a problem using repeated sampling experiments and observing the proportion of times some property is satisfied. Estimates of probabilities may be computed by running a simulation repeatedly and using the relative frequency. Statistics Methods that use pseudo-random numbers in order to determine the properties of some function or set of functions. For example, the acceptance–rejection algorithm can be used to evaluate integrals, simulation makes use of pseudo-random numbers to study the outcome of some complex system, and Monte Carlo tests use pseudo-random numbers to decide on the outcome of hypothesis tests. The accuracy of the methods generally improves with an increase in the number of pseudo-random numbers used. Computer Numerical methods in which randomly generated numbers play a part in the calculations. A probabilistic model is constructed, corresponding to the mathematical or physical problem, and random samples are taken within the model. By taking more samples, a more accurate estimate of the result is obtained. Such methods are used for example on problems in particle physics, evaluation of multiple integrals, traffic problems, and large-scale operational problems generally. See also stochastic process.
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