Patent ID: 7774293

Claim:
A method for determining via a computing processor a probability associated with a causal scenario including an initiating event, at least one pivotal event and an end state, the method comprising: modeling the causal scenario by a first causal model characterized by a plurality of first nodes interconnected one with another to define a termination of the causal scenario in the end state via a Boolean state of at least one variable associated with said plurality of first nodes; modeling factors affecting said Boolean state of said at least one variable at a corresponding one of said first nodes of said first causal model by a second causal model characterized by a plurality of second nodes, each of said second nodes representing a corresponding multistate variable indicative of an attribute of said factors, said plurality of second nodes being interconnected one with another in accordance with a joint probability distribution of said multistate variables, wherein said joint probability distribution is defined by a probabilistic network coupled to said second causal model for characterizing said factors, and wherein said second causal model includes at least one node corresponding to said at least one variable, said probabilistic network directly providing to at least one of said plurality of first nodes at least one second factor associated with said at least one pivotal event; constructing from said first causal model and said second causal model a hybrid computational model executable on the processor; and executing said hybrid computational model on the processor to determine a probability of said at least one pivotal event of the causal scenario from a probability of said Boolean state of said at least one variable of said first causal model as determined from a probability of said factors affecting said Boolean state of said at least one variable calculated by said second causal model in accordance with said joint probability distribution of said multistate variables defined by said probabilistic network.