Author(s)

A. T. Adeniran¹, O. Faweya^2*, T. O. Ogunlade³, K. O. Balogun⁴

¹Department of Statistics, University of Ibadan, Ibadan, Nigeria.
²Department of Statistics, Ekiti State University (EKSU), Ado-Ekiti, Nigeria.
³Department of Mathematics, Ekiti State University (EKSU), Ado-Ekiti, Nigeria.
⁴Department of Statistics, Federal School of Statistics, Ibadan, Nigeria.

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.

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

Adeniran, A. , Faweya, O. , Ogunlade, T. and Balogun, K. (2020) Derivation of Gaussian Probability Distribution: A New Approach. Applied Mathematics, 11, 436-446. doi: 10.4236/am.2020.116031.