Most of the nuclear accident reports used to indicate the implicit precursors which are not quantified earlier as underlying factors. The current Probabilistic Safety Assessment is capable of quantifying the importance of accident causes in limited scope. It was, therefore, difficult to achieve quantifiable decision making for resource allocation. In this study, the methodology which facilitates to quantify these precursors and a case study of Fukushima accident has been presented. First, four implicit precursors have been obtained by evaluating the causality and hierarchy structure of various accident factors. Eventually, it turned out they represent the lack of knowledge. After four precursors are selected, sub-precursors have been investigated and their cause-consequence relationship has been implemented by using Bayesian Belief Network (BBN). To prioritize the precursors, the prior probability is initially estimated by expert judgment and updated upon observations. The pair-wise importance between precursors is calculated by Analytic Hierarchy Process (AHP) and the results are converted into Node Probability Tables in the BBN model. Using this method, the sensitivity and the posterior probability of each precursor can be analyzed so that it enables to make prioritization for the factors. The lessons learned from Fukushima accident to demonstrate the feasibility of the proposed methodology have been implemented. Copyright (c) 2014 John Wiley & Sons, Ltd.