Among the notable shortcomings of this small basis parameterization, it is difficult to ascribe clear meaning to the values in the resulting spectral vector. Least-squares fitting using a small basis can exploit small differences of large amplitudes to accommodate physically inconsequential differences between the original and the modeled decay curves. This can lead to coefficients that oscillate, thereby obscuring the physical meaning of the parameters. This shortcoming can be alleviated, and many advantages accrued, if the basis set is appropriately orthonormalized before fitting to the data. For orthonormal basis parameterization (OBP), the fitting function given by Equation (1.4) is modified according to Equation (1.6) to give One advantage of OBP is that the resulting parameter values are uncorrelated. A second advantage is that, in the Bayesian perspective, the probability distributions for all the orthogonal basis parameter values have the same variance, equal to the noise variance of the data. Another advantage is that each parameter is the scalar product of the orthogonalized basis function with the decay curve and is in that sense a functi0nal of the time-domain data. As will be illustrated, these parameters are simultaneously functionals of the underlying distribution of relaxation rates, and in that sense have discernable physical meaning. The use of spectral-domain and time-domain functionals to parameterize and exploit multicomponent relaxation analysis is well established. Often, the functionals are calculated based on ILT-like interpretations of the data . The recent use of logarithmic moments calculates useful functionals directly from the data .