  Radon--Nikodym approach to relaxation dynamics, where probability density is
built first and then used to calculate observable dynamic characteristic is
developed and applied to relaxation type signals study. In contrast with $L^2$
norm approaches, such as Fourier or least squares, this new approach does not
use a norm, the problem is reduced to finding the spectrum of an operator
(virtual Hamiltonian), which is built in a way that eigenvalues represent the
dynamic characteristic of interest and eigenvectors represent probability
density. The problems of interpolation (numerical estimation of Radon--Nikodym
derivatives is developed) and obtaining the distribution of relaxation rates
from sampled timeserie are considered. Application of the theory is
demonstrated on a number of model and experimentally measured timeserie signals
of degradation and relaxation processes. Software product, implementing the
theory is developed.
