2. The method of claim 1 wherein the confidence indicator is calculated in accordance with: 
 confidence indicator, 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.
1. A method comprising: training a computer implemented Bayesian based diagnostic system, wherein training 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; generating a confidence indicator corresponding to the new diagnosis, wherein the confidence indicator is generated on a per-diagnosis basis; comparing the confidence indicator to a predetermined threshold; using the diagnostic system to obtain another diagnosis using other input data in response to determining the confidence indicator of the new diagnosis is greater than a predetermined threshold, the another diagnosis including a corresponding accuracy measure; and generating a cumulative confidence indicator for multiple per-diagnosis confidence intervals, the cumulative confidence indicator including the determined confidence indicator.