Bayesian Approach to Generalized Normal Distribution under Non-Informative and Informative Priors | Zendy

Saima Naqash | Zendy; S.P. Ahmad | Zendy; Aquil Ahmed | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Bayesian Approach to Generalized Normal Distribution under Non-Informative and Informative Priors

Author(s) -

Saima Naqash,

S.P. Ahmad,

Aquil Ahmed

Publication year - 2018

Publication title -

international journal of mathematical sciences and computing

Language(s) - English

Resource type - Journals

eISSN - 2310-9033

pISSN - 2310-9025

DOI - 10.5815/ijmsc.2018.04.02

Subject(s) - prior probability , generalized normal distribution , scale parameter , normal distribution , bayesian probability , distribution (mathematics) , mathematics , computer science , estimation theory , conjugate prior , scale (ratio) , shape parameter , statistics , generalized gamma distribution , bayes estimator , posterior predictive distribution , maximum likelihood , categorical distribution , pattern recognition (psychology) , bayesian linear regression , gamma distribution , artificial intelligence , bayesian inference , mathematical analysis , physics , quantum mechanics

The generalized Normal distribution is obtained from normal distribution by adding a shape parameter to it. This paper is based on the estimation of the shape and scale parameter of generalized Normal distribution by using the maximum likelihood estimation and Bayesian estimation method via Lindley approximation method under Jeffreys prior and informative priors. The objective of this paper is to see which is the suitable prior for the shape and scale parameter of generalized Normal distribution. Simulation study with varying sample sizes, based on MSE, is conducted in R-software for data analysis.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research