On the Usefulness of the Maximum Entropy Principle in the Bayesian Estimation of Reliability

The use of the maximum entropy principle for determining prior distribution is compared with other techniques in statistical-decision theory for estimating reliability. The comparison is made in the context of estimating the parameter representing the probability of success in a binomial model and the parameter representing the mean time to failure in a simple exponential model. The existence of partial prior information of varying degrees is also assumed. When a quadratic loss function is used, the techniques based on the maximum entropy principle lose much of their appeal.