  We study the inverse problem of deducing the dynamical characteristics (such
as the potential field) of large systems from kinematic observations. We show
that, for a class of steady-state systems, the solution is unique even with
fragmentary data, dark matter, or selection (bias) functions. Using spherically
symmetric models for simulations, we investigate solution convergence and the
roles of data noise and regularization in the inverse problem. We also present
a method, analogous to tomography, for comparing the observed data with a model
probability distribution such that the latter can be determined.
