������Pareto�ֲ��¿������������ݵı�Ҷ˹ͳ�Ʒ���.
ͨ�����븨���������̻�ʧЧ��ԭ��, �Ӷ�����Ȼ����,
��ʹ�ñ�Ҷ˹����㱴Ҷ˹�Լ����鱴Ҷ˹���ַ��������Ʋ���,
Ȼ��ͨ��һ��ʵ�����Ƚ����ַ���������, ��������˱�������ֲ��г�����ѡȡ�ķ���.

In this paper Bayesian statistical analysis of masked data is
considered based on the Pareto distribution. The likelihood function is simplified by
introducing auxiliary variables, which describe the causes of failure. Three Bayesian
approaches (Bayes using subjective priors, hierarchical Bayes and empirical Bayes) are
utilized to estimate the parameters, and we compare these methods by analyzing a real
data. Finally we discuss the method of avoiding the choice of the hyperparameters in
the prior distributions.