8. A system comprising: a computer implemented Bayesian diagnostic system, wherein the diagnostic system includes an inferencing engine and a conditional probability table stored on a non-transitory storage device that forms the basis for Bayesian inferences once the diagnostic system is trained, each inference including a diagnosis and associated probability of the diagnosis, wherein training the diagnostic system includes providing the diagnostic system with historical input data and a corresponding diagnosis to be derived from the historical input data and providing new input data having a predetermined diagnosis, wherein the diagnostic system generates a conditional probability table of a new diagnosis using the new input data; and a confidence generator stored on a non-transitory storage device to receive the inferences, and generate a confidence measure for each inference, wherein the confidence indicator is generated on a per-inference basis and wherein the confidence generator further generates a cumulative confidence measure for multiple per-inference confidence intervals, and wherein the diagnostic system is trained when the confidence indicator of the new diagnosis is greater than a predetermined threshold.
8. A system comprising: a computer implemented Bayesian diagnostic system, wherein the diagnostic system includes an inferencing engine and a conditional probability table stored on a non-transitory storage device that forms the basis for Bayesian inferences once the diagnostic system is trained, each inference including a diagnosis and associated probability of the diagnosis, wherein training the diagnostic system includes providing the diagnostic system with historical input data and a corresponding diagnosis to be derived from the historical input data and providing new input data having a predetermined diagnosis, wherein the diagnostic system generates a conditional probability table of a new diagnosis using the new input data; and a confidence generator stored on a non-transitory storage device to receive the inferences, and generate a confidence measure for each inference, wherein the confidence indicator is generated on a per-inference basis and wherein the confidence generator further generates a cumulative confidence measure for multiple per-inference confidence intervals, and wherein the diagnostic system is trained when the confidence indicator of the new diagnosis is greater than a predetermined threshold. 9. The system of claim 8 wherein the confidence generator is operable to calculate the confidence measure in accordance with: 
 confidence, c ij =1− p ij (1− p ij )/{α(log N ij )} where, p ij =prior probability of test T j failing when component B i is bad N ij =number of observations involving T j and B i , and α=a constant to normalize variance between 0 and 1.