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Young, G.A. and Smith, R.L. (2005) Essentials of Statistical Inference. Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press, Cambridge.
https://doi.org/10.1017/CBO9780511755392

has been cited by the following article:

• TITLE: Derivation of Gaussian Probability Distribution: A New Approach

  AUTHORS: A. T. Adeniran, O. Faweya, T. O. Ogunlade, K. O. Balogun

  KEYWORDS: De Moivres Laplace Limit Theorem, Binomial Probability Mass Function, Gaussian Distribution, Random Experiment

  JOURNAL NAME: Applied Mathematics, Vol.11 No.6, June 1, 2020

  ABSTRACT: 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.