Measurements are produced by solving the parameters listed in the right column of Table 4 and then calculating $M(x,y)$ using Eq. (11). This is done by the following process. First, let us postulate that the terms in the right column of Table 4 are known. Whether true or not, let us use this postulate to make the following points. Assuming that these values are known, then $fc(x,y;t)$ can be calculating by inserting $m(x,y)$ and $\sigma (x,y)$ into Eq. (11) to calculate $M(x,y)$ and then substituting all of these terms into Eq. (9). In this hypothetical situation, $fc(x,y;t)$ is a theoretical calculation of the collected movie that would occur if all the underlying parameters were known and there were no sources of noise. Now consider a real movie that is collected during an examination, called $nc(x,y;t)$. Even if the underlying parameters that caused $fc(x,y;t)$, are the same ones that cause $nc(x,y;t)$, $nc(x,y;t)$ will never be exactly equal to $fc(x,y;t)$ because $nc(x,y;t)$ contains noise. If we were able to find a set of parameters (see column B of Table 4) that cause the model $fc(x,y;t)$ to be equal to $nc(x,y;t)$, then we might be convinced that the parameters are correct. However, the reality is that no matter what, $fc(x,y;t)\u2009$ will never be equal to $nc(x,y;t)$ because the SLO image is noisy. The photodetector and other electronics produce noisy signals, and there is stray light, among other factors. The best we can do is to cause $fc(x,y;t)$ to be a reasonable fit to (i.e., close to but not equal to) $nc(x,y;t)$. A mathematical tool for fitting two functions while not forcing them to be equal is the I-divergence objective function,^{21}^{,}^{22} described next. The I-divergence value is a function of the model and true data and it has its lowest value (i.e., it is minimized) when these two functions have their best fit.