This paper presents a novel approach to determine the sensitization probability of a non-robustly testable path using probability density functions (PDFs). The proposed approach systematically refines a set of pat- terns that sensitize the path non-robustly which initial set has been derived with existing methods, and is kept im- plicitly. Accurate measure of the sensitization probability is obtained fast by avoiding Monte-Carlo. It is shown ex- perimentally that the proposed approach is accurate and much faster than Monte-Carlo, and thus can be used to rank a collection of non-robust paths considering their sen- sitization characteristics.