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[en] Traditional Probabilistic Risk Assessments (PRAs) model dependencies through deterministic relationships in fault trees and event trees and parametric models of common cause failure (CCF) events. Popular CCF models do not recognize system specific defenses against dependencies and are restricted to identical components in redundant configurations. While this has allowed prediction of system reliability with little or no data, it is a limiting factor in many applications such as modeling the characteristics of a specific system design or incorporating the characteristics of failure when assessing a failure event's risk significance (known as an Event Assessment, or Significance Determination). This paper proposes the General Dependency Model (GDM), which uses Bayesian Network to model the probabilistic dependencies between components. This is done through the introduction of three parameters for each failure cause, which relate to physical attributes of the system being modeled, i.e., cause condition probability, component fragility, and coupling factor strength. Finally this paper demonstrates the development and use of the GDM in traditional applications of PRA, and for Event Assessments and Significance Determination. Examples of the quantification of the GDM model parameters for these applications are provided. - Highlights: • Current common cause failure modeling methods are limited to identical components. • Current Event Assessments cannot include information about the failure cause. • A General Dependency Model (GDM) is proposed using Bayesian Network modeling. • GDM can use historic data and causal information from qualitative assessments.