Comparing conditional probabilities and statistical independence in layers of protection analysis

Abstract

This paper demonstrates an approach to include conditional probabilities when calculating probabilities of disaster in quantitative risk analysis. Since not all layers of protection are independent of each other, using conditional probabilities leads to more accurate calculations of probabilities of disaster in a chemical process. An event tree analysis approach is shown by calculating probabilities of disaster assuming that events are statistically independent (independence assumption), and separately considering that events affect each other (conditional probabilities), in three cases with increasing complexity and number of layers of protection. For each case, we considered multiple scenarios to show that results do not depend on the input probabilities of particular events. In most scenarios of the three cases, findings demonstrate that the probabilities of disaster calculated using the independence assumption are lower than those with conditional probabilities, which can lead to undercounting the layers of protection that are actually needed. K E Y W O R D Sevent tree analysis, independent layers of protection (IPLs), layers of protection analysis (LOPA), quantitative risk analysis (QRA)

An Experiment on University English Translation Teaching Model Based on Big Data-Driven Learning

“…Where 𝑦 𝑖 is the value of sample 𝑦 at attribute 𝐴 𝑖 and 𝑃(𝑦 𝑖 | 𝐶 𝑗 ) is the conditional probability that sample 𝑦 takes value 𝑦 𝑖 at attribute 𝐴 𝑖 for a given class 𝐶 𝑗 [20].…”