Bayesian Estimation of Entropy for Kumaraswamy Distribution and Its Application to Progressively First-Failure Censored Data

Entropy can be mathematically defined as a measure of the uncertainty random variables that represents potential quantity information. This article investigates behavior entropy which follow Kumaraswamy distribution using progressively first-failure censored (PFFC) data. In particular, we calculate maximum likelihood estimation and confidence interval by observed Fisher information matrix through asymptotic estimator. Furthermore, apply Markov Chain Monte Carlo (MCMC) method help us to estimates formulation credible intervals in order address this problem. Here, numerical example real data is presented illustrate performance proposed method. Finally, perform simulations observe procedure.