  So far, various techniques have been implemented for generating discrete
distributions based on continuous distributions. The characteristics and
properties of this kind of probability distributions have been studied.
Furthermore, the estimation of related parameters have been computed trough
classical methods. However, a few studies addressed the parameter estimate
issue of these distributions through Bayesian methods. This is essentially
because of the complexity of the model whatever the number of parameter is and
the fact that in general they contain a large number of parameters to be
estimated. This paper deals with computing Bayes estimate of the parameters of
discrete Burr distribution with two parameters. Since the resulting posterior
distribution of the parameters is not standard, we apply Metropolis-Hastings
algorithm to simulate from the posterior density.
