Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Combining Various Solution Techniques for Dynamic Fault Tree Analysis of Computer Systems
HASE '98 The 3rd IEEE International Symposium on High-Assurance Systems Engineering
Analysis in HUGIN of data conflict
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
The Galileo Fault Tree Analysis Tool
FTCS '99 Proceedings of the Twenty-Ninth Annual International Symposium on Fault-Tolerant Computing
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In this study, an alternative approach for combining Fault Trees (FT) and Event Trees (ET) using capabilities of Bayesian networks (BN) for dependency analysis is proposed. We focused on treating implicit and explicit weak s-dependencies that may exist among different static/dynamic FTs related to an ET. In case of combining implicit s-dependent static FTs and ET that combinatorial approaches fail to get the exact result, the proposed approach is accurate and more efficient than using Markov Chain (MC) based approaches. In case of combining implicit weak s-dependent dynamic FTs and ET where the effect of implicit s-dependencies have to be manually inserted into the MC, the proposed approach is more efficient for getting an acceptable result.