Measurements are produced by solving the parameters listed in the right column of Table 4 and then calculating 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 can be calculating by inserting and into Eq. (11) to calculate and then substituting all of these terms into Eq. (9). In this hypothetical situation, 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 . Even if the underlying parameters that caused , are the same ones that cause , will never be exactly equal to because contains noise. If we were able to find a set of parameters (see column B of Table 4) that cause the model to be equal to , then we might be convinced that the parameters are correct. However, the reality is that no matter what, will never be equal to 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 to be a reasonable fit to (i.e., close to but not equal to) . 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.