This paper presents a hierarchical or multistage empirical Bayesian ap
proach that has been used for the estimation of uncertainty about comp
onent failure rates presented in the T-Book, a handbook concerning rel
iability data of components in Nordic nuclear power plants. The failur
e process is basically assumed to be a homogeneous Poisson process. A
class of contaminated gamma distributions is considered to describe th
e uncertainty concerning the intensity of this process. These distribu
tions in turn are defined through a set of hyperparameters, the knowle
dge of which is also described and updated via Bayes formula. The prin
ciple of Data Translated Likelihood is used for the derivation of non-
informative prior distribution for the hyperparameters. The resulting
Non-informative Contaminated Gamma-Poisson approach is illustrated by
an example, the result of which is compared to alternative models. An
application to a 2D problem is also presented.