## 3 Results  
Based on the previous model and data received, the CPTs for the previously described model were quantified. Where there was evidence, a frequentist approach of determining probabilities was used; however, when that data was not available, appropriate approximations were used to complete the table that would minimize influencing the posterior estimates to a greater extent than the available information. Table 2 is an example of one of the quantified dynamic CPTs. In each instance, the majority of data was classified in the same bin as the previous measurement; any deviation would therefore be considered a rare event and worth noting. Although certain relationships might not occur in an actual accident scenario, those elements still need to be included in the CPTs. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Parameter** & **Low Threshold** & **High Threshold** \\ \hline Inlet Temperature (K) & 667.3 & 668.2 \\ Outlet Temperature (K) & 798.2 & 821.3 \\ Relative Power-to-Flow Ratio (s/kg) & 12.85 & 12.98 \\ Inlet Pressure (Pa) & 235,019 & 235,126 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model parameters and discretized bin threshold values based on baseline operational data. Figure 6: Data from different models with varying time frequencies are compiled into a single operational timeline. Given multiple accident scenarios, this produces many example operational timelines, which can be used for constructing the DBN’s conditional probability tables. To show the model's effectiveness at assessing an SFR's health state following an accident event and potential future failure outcomes, hypothetical data are provided to the model that may be indicative of a TOP. These data will serve as evidence, which will impact the posterior estimates for the system prognostics, diagnostics, and accident state. A transient overpower could be initially indicated by an increase in the power-to-flow ratio. Table 3 shows the prior and posterior scram state probabilities with evidence that the power-to-flow ratio was found to be higher than a baseline amount and all other relevant parameters were held the same. As expected, the prior probabilities of the scram state and the power-to-flow CPTs along with the limited amount of information provided little change to the prior; this model is still predominantly assessing that the scram process is working as intended. Although minute, the posterior estimates for the state of the scram and trip mechanisms are changing. However, just as in most instances, that information alone is not enough to convince the model that something is seriously going wrong. There are many reasons that the power-to-flow ratio may be higher than baseline measurements; given the significantly low probability that the scram and trip mechanism fails, the model is estimating that there is something else that could explain the discrepancy. This is also seen in the system prognostics at this particular point in time, as shown in Table 4. For both instances, the probability of failure is either non-existent or negligible. This assessment of the reactor's prognostics changes, however, when new information is received. Assume now that the following sensor readings indicate that not only is the power-to-flow value still above the operational baseline, but the outlet temperature of the reactor is also higher than anticipated. This is then followed by another reading of high power-to-flow and outlet and inlet temperatures. This combination of evidence, in addition to the initial high power-to-flow value, significantly alters the estimate of whether the scramming mechanism worked, as seen in Table 5. The posterior estimates indicate that it is more likely that the scram and trip failed. The model responds to a small amount of information to raise a concern that an accident has indeed occurred.