Modified Monte Carlo Method for Triple Integral
Модифицированный метод Монте‑Карло для тройного интеграла
2011-09-01
SCID: 54.1/as8acnen
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Monte Carlo methodcentral limit theoremmodified Monte Carlosampling from integration regiontriple integral
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Abstract (AI)
In order to calculate triple integral problem, Monte Carlo method is used in this paper. By introducing n random points from the right area V, traditional algorithm was changed to select points directly from the integral region O. The principle and realization steps of modified algorithm were shown. Using this method, m random numbers could be generated, and it is obey approximately uniform distribution in O. At last, an actual case shows that this algorithm is prior and the distribution of integral values obtained by modified method conforms to central limit theorem. All these can be proved by experimental data and the fitting curve.
Key Findings
1
A modified Monte Carlo algorithm selects n random points directly from the integration region O rather than from a superset V.
2
An example case demonstrates the modified algorithm outperforms the traditional approach (algorithm is prior).
3
The distribution of integral estimates produced by the modified method conforms to the central limit theorem, supported by experimental data and fitting curves.
4
The modified method generates m random numbers that are approximately uniformly distributed within region O.
Research Object
Triple integral evaluation region O (integration region for a triple integral)
Research Subject
Modified Monte Carlo sampling algorithm that selects n (or m) random points uniformly in region O to compute triple integrals and its resulting distribution of integral estimates (conformity to the central limit theorem)
Publication Details
Publication Date
2011-09-01
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