Abstract: A hybrid causal framework applies properties of probabilistic models, such as Bayesian belief networks, to causal logic models, such as fault trees and event sequence diagrams. The probabilistic model establishes a joint probability distribution of causal relationships between events and conditions in the logic models. The probability of the events and conditions are found by propagating probabilities from the probabilistic model through the logic models.

Abstract: A computer readable medium includes one or more sequences of one or more instructions for execution by one or more processors. The instructions when executed by the one or more processors cause the one or more processors to automatically insert common cause basic events into multiple tree structures in a risk model stored in a computer memory. The multiple tree structures are based on a single definition of the common cause failure group. The processors also replace regular basic events within the multiple structures with logigates including all common cause basic events associated with the common cause failure group.

Abstract: A computer readable medium includes one or more sequences of one or more instructions for execution by one or more processors. The instructions when executed by the one or more processors cause the one or more processors to automatically insert common cause basic events into multiple tree structures in a risk model stored in a computer memory. The multiple tree structures are based on a single definition of the common cause failure group. The processors also replace regular basic events within the multiple structures with logigates including all common cause basic events associated with the common cause failure group.