Bayes results for classical Pareto distribution via Gibbs sampler, with doubly-censored observations

The paper considers the full Bayes analysis of the Pareto distribution when the observations are doubly censored, and provides sample-based estimates of posterior distributions using Gibbs sampler algorithm. The approach is not only computationally simple but fully explores the low-dimensional posterior surfaces-which otherwise seems difficult. Complexities through censored data always arise in life testing experiments; these complexities are no longer problems with the Gibbs sampler algorithm, unlike the situations with nonsample-based approaches.