  We present a quasi-conjugate Bayes approach for estimating Generalized Pareto
Distribution (GPD) parameters, distribution tails and extreme quantiles within
the Peaks-Over-Threshold framework. Damsleth conjugate Bayes structure on Gamma
distributions is transfered to GPD. Posterior estimates are then computed by
Gibbs samplers with Hastings-Metropolis steps. Accurate Bayes credibility
intervals are also defined, they provide assessment of the quality of the
extreme events estimates. An empirical Bayesian method is used in this work,
but the suggested approach could incorporate prior information. It is shown
that the obtained quasi-conjugate Bayes estimators compare well with the GPD
standard estimators when simulated and real data sets are studied.
