EnhancedBayesianNetworks.jl: A New Julia Framework for Multi Scenario Risk Assessment

Risk assessment is essential for identifying vulnerabilities, preventing disruptions, and enhancing decision-making processes. Traditional reliability evaluation techniques, such as Fault-Tree analysis, have been extensively used but have limitations in handling continuous random variables and uncertainties. Bayesian Networks, with their ability to integrate multidisciplinary analyzes through a graph structures based on conditional dependencies, offer a robust alternative. However, traditional Bayesian Networks are inadequate for modeling small probability values and uncertainties. Enhanced Bayesian Networks address these limitations by incorporating Structural Reliability Methods, allowing the usage of continuous random variables with arbitrary distributions and interdependencies. This makes enhanced Bayesian Networks a powerful framework for a more comprehensive and reliable risk analysis across diverse scenarios. The EnhancedBayesianNetworks.jl library, implemented in Julia, leverages Julia's high-performance computing capabilities to efficiently perform advanced Monte Carlo simulations and evaluate enhanced Bayesian Networks Conditional Probability Tables.