I know only a little about Fisher information and optimal experimental design, but I'm trying to better understand the subject. If I have an experiment composed of a single detector and my detector location is along a single axis and is parameterized by a scalar $\theta$ (my explanatory variable), then my D-Criterion would be $D=\text{det}(I^T I)$, where $I =I(\theta)$ is the Fisher information matrix. Since the experiment depends on a single parameter it would just be $D=I^2(\theta)$. If the goal for the optimal experiment is to maximize $D$, then what is this doing to the probability distribution for an observable $X$? It seems like $f(X;\theta)$ is tuned to have some characteristic property to it, but what would that be?
If there's anything off about my example/reasoning please correct me.