“…Throughout the remainder of this work, we will denote the log-scaled parameter vector (for N p = 1 and κ = 6) as
Thus, as long as we define our cost function to be a continuous function of the parameters, we know the inverse problem has a solution (minimizing a continuous function on a compact parameter space). One could broaden this parameter estimation framework to the distributional case if desired, taking an admissible parameter space as a compact subset of Euclidean space (including all parameters excuding relaxation times) along with the space of probability measures, and use the Prohorov metric framework (see, e.g., [7, 15, Sec. 4]) and the approximation results of [9].…”