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Derivation of Gaussian Probability Distribution: A New Approach 被引量:2
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作者 A. T. Adeniran O. Faweya +1 位作者 T. O. Ogunlade K. O. Balogun 《Applied Mathematics》 2020年第6期436-446,共11页
The famous de Moivre’s Laplace limit theorem proved the probability density function of Gaussian distribution from binomial probability mass function under specified conditions. De Moivre’s Laplace approach is cumbe... The famous de Moivre’s Laplace limit theorem proved the probability density function of Gaussian distribution from binomial probability mass function under specified conditions. De Moivre’s Laplace approach is cumbersome as it relies heavily on many lemmas and theorems. This paper invented an alternative and less rigorous method of deriving Gaussian distribution from basic random experiment conditional on some assumptions. 展开更多
关键词 De Moivres Laplace Limit Theorem binomial probability mass function Gaussian Distribution Random Experiment
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