An Estimation of Unknown Variance of a Normal Distribution: Application to Borno State Rainfall Data
International Journal of Data Science and Analysis
Volume 5, Issue 1, February 2019, Pages: 1-5
Received: Dec. 26, 2018; Accepted: Feb. 15, 2019; Published: Mar. 28, 2019
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Adegoke Taiwo Mobolaji, Department of Statistics, University of Ilorin, Ilorin, Nigeria
Nicholas Pindar Dibal, Department of Mathematical Sciences, University of Maiduguri, Maiduguri, Nigeria
Yahaya Abdullahi Musa, Department of Mathematical Sciences, University of Maiduguri, Maiduguri, Nigeria
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The Bayesian estimation of unknown variance of a normal distribution is examined under different priors using Gibbs sampling approach with an assumption that mean is known. The posterior distributions for the unknown variance of the Normal distribution were derived using the following priors: Inverse Gamma distribution, Inverse Chi-square distribution and Levy distribution of the unknown variance of a normal distribution and Gumbel Type II. R functions are developed to study the various statistical simulation samples generated from Winbugs.
Normal Distribution, Prior Distribution, Posterior Distribution, Bayesian Estimation, Inverse Gamma Distribution, Inverse Chi-Square Distribution, Levy Distribution, Gumbel Type II Distribution
To cite this article
Adegoke Taiwo Mobolaji, Nicholas Pindar Dibal, Yahaya Abdullahi Musa, An Estimation of Unknown Variance of a Normal Distribution: Application to Borno State Rainfall Data, International Journal of Data Science and Analysis. Vol. 5, No. 1, 2019, pp. 1-5. doi: 10.11648/j.ijdsa.20190501.11
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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