The primary foal of the paper is to provide a framework for future research in generalizing Sainsbury's approach by considering (k, L) to be a random vector described by a joint probability density function and by allowing broader classes of distributions to be considered. Minimum cross-entropy inversion, an information-theoretic methodology for approximating probability distributions, is shown to be effective in selecting a reasonable and unique joint distribution corresponding to observable length moments.