Derivation of Gaussian Probability Distribution: A New Approach

• 作者: A. T. Adeniran K. O. Balogun O. Faweya T. O. Ogunlade
• 来源期刊: 应用数学(英文)
• 学科: 数学
• 关键词: De Moivres Laplace Limit Theorem Binomial Probability Mass Function Gaussian Distribution Random Experiment
• 年，卷（期）: 2020,（6）
• 页码范围: 436-446
• 页数: 11页
• 分类号: O17

摘要

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.