A method for analyzing a complex system comprising a number of subsystems, wherein each subsystem comprises at least one sensor designed to generate sensor data, the method comprising: executing a built-in-test on sensor data from at least one of the sensors to generate binary evidence of a sensed event, wherein the binary evidence comprises: BIT evidence generated by a particular built-in-test, wherein the BIT evidence implicates an ambiguity group of failure modes; processing sensor data from at least one of the sensors to generate complex evidence of the sensed event, wherein the complex evidence has more sophisticated mathematical properties than the binary evidence, wherein the complex evidence comprises: a condition indicator that comprises derived parametric data that provides partial evidence towards an ambiguity group of failure modes, a health indicator that comprises partially-summarized diagnostic conclusions that contain a reference to an ambiguity group of failure modes and provide probabilistic evidence for an ambiguity group of failure modes, and a prognostic indicator that comprises partially-summarized evidences that indicate a future evolution of the HI over a future time period; providing a system fault model that defines statistical relationships between binary evidence, complex evidence, and an underlying failure mode that is occurring in the complex system; processing, via a processor, the binary evidence and the complex evidence to identify failure modes taking place within one or more of the subsystems; and generating, based on the binary evidence and the complex evidence and the SFM, diagnostic conclusions regarding adverse events that are taking place within the complex system, and prognostic conclusions regarding adverse events that are predicted to take place within the complex system. executing a built-in-test on sensor data from at least one of the sensors to generate binary evidence of a sensed event, wherein the binary evidence comprises: BIT evidence generated by a particular built-in-test, wherein the BIT evidence implicates an ambiguity group of failure modes; processing sensor data from at least one of the sensors to generate complex evidence of the sensed event, wherein the complex evidence has more sophisticated mathematical properties than the binary evidence, wherein the complex evidence comprises: a condition indicator that comprises derived parametric data that provides partial evidence towards an ambiguity group of failure modes, a health indicator that comprises partially-summarized diagnostic conclusions that contain a reference to an ambiguity group of failure modes and provide probabilistic evidence for an ambiguity group of failure modes, and a prognostic indicator that comprises partially-summarized evidences that indicate a future evolution of the HI over a future time period; a condition indicator that comprises derived parametric data that provides partial evidence towards an ambiguity group of failure modes, a health indicator that comprises partially-summarized diagnostic conclusions that contain a reference to an ambiguity group of failure modes and provide probabilistic evidence for an ambiguity group of failure modes, and a prognostic indicator that comprises partially-summarized evidences that indicate a future evolution of the HI over a future time period; providing a system fault model that defines statistical relationships between binary evidence, complex evidence, and an underlying failure mode that is occurring in the complex system; processing, via a processor, the binary evidence and the complex evidence to identify failure modes taking place within one or more of the subsystems; and generating, based on the binary evidence and the complex evidence and the SFM, diagnostic conclusions regarding adverse events that are taking place within the complex system, and prognostic conclusions regarding adverse events that are predicted to take place within the complex system.