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To make accurate predictions of attributes like defects found in complex software projects we need a rich set of process factors. We have developed a causal model that includes such process factors, both quantitative and qualitative. The factors in the model were identified as part of a major collaborative project. A challenge for such a model is getting(More)
We present a hybrid Bayesian network (HBN) framework to analyse dynamic fault trees. By incorporating a new approximate inference algorithm for HBNs involving dynamically discretising the domain of all continuous variables, accurate approximations for the failure distribution of both static and dynamic fault tree constructs are obtained. Unlike in other(More)
Although Bayesian networks (BNs) are increasingly being used to solve real-world risk problems, their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognizing that it is rarely cost effective to elicit complete sets(More)
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