Regularization can be understood from this perspective as choosing values of these ill-defined parameter combinations to suppress undesirable features appearing in the spectrum of relaxation rates. In this view, the highly parsimonious spike representations resulting from ARM+LSQ are objectively regularized because all undefinable parameter combinations are set to zero. However, by tradition, and in the sense used in this report, regularization generates smooth, sometimes broad distributions of relaxation rates, even if the data come from discrete underlying source distributions. The widths of peaks in the distributions depend not only on the source distributions and the noise, but also on the regularization algorithm and the degree of regularization applied.