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International Journal of Applied & Experimental Mathematics Volume 1 (2016), Article ID 1:IJAEM-103, 4 pages
https://doi.org/10.15344/2456-8155/2016/103
Research Article
Probability Distributions for the Mean and Variance Using Maximum Entropy and Bayesian Analysis

Bahman Shafii* and William J. Price

Statistical Programs, P.O. Box 442337, University of Idaho, Moscow, ID, 83844-2337, USA
Dr. Bahman Shafii, Statistical Programs, P.O. Box 442337, University of Idaho, Moscow, ID, 83844-2337 USA; E-mail: bshafii@uidaho.edu
20 November 2015; 04 February 2016; 06 February 2016
Shafii B, Price WJ (2016) Probability Distributions for the Mean and Variance Using Maximum Entropy and Bayesian Analysis. Int J Appl Exp Math 1: 103. doi: https://doi.org/10.15344/2456-8155/2016/103

Abstract

Estimation of moments such as the mean and variance of populations is generally carried out through sample estimates. Given normality of the parent population, the distribution of sample mean and sample variance is straightforward. However, when normality cannot be assumed, inference is usually based on approximations through the use of the Central Limit theorem. Furthermore, the data generated from many real populations may be naturally bounded; i.e., weights, heights, etc. Thus, a normal population, with its infinite bounds, may not be appropriate, and the distribution of sample mean and variance is not obvious. Using Bayesian analysis and maximum entropy, procedures are developed which produce distributions for the sample mean and combined mean and standard deviation. These methods require no assumptions on the form of the parent distribution or the size of the sample and inherently make use of existing bounds.