Bayesian estimation under Kullback-Leibler divergence measure based on exponential data

In information theory, Kullback-Leibler divergence measure is a commonly used difference measure that is used for computing the distance between two probability distributions. In this paper, we apply Kullback-Leibler divergence measure between actual and approximate distribution to drive a loss function. We then apply the derived loss function on Exponential distribution to find the Bayes estimate of the parameter θ, and compare it with the Bayes estimate obtained using square error loss function. Our comparisons between these two estimates are based on complete, type II censoring and type I censoring data.