BAYESIAN STUDY OF NORMAL SEQUENCE APPLYING A PRIOR MIXTURE TO THE LOCATION PARAMETER

Abstract

This article discusses Bayesian Analysis of Normal Sequence using Mixture of Priors. Using the Bayesian methodology, one can generate Bayes estimates by assuming a newly created Mixture combination of priors for location and correct prior for scale parameters. In order to get Bayes estimates for our study, we have assumed that one innovative form of prior, such as a double exponential prior combined with the usual type prior, viz., Normal prior for the mean parameter and correct prior, viz., Inverted gamma prior for the location parameter. Examples of the recently developed methodology in numerical studies are shown by the mean square error of the Bayes estimates of both parameters computed with different known and unknown nature of the parameters and results given with full discussion.