in which it was shown that maximizing entropy provides the least-biased
statistical inference when insufficient information is available. In References [11, 12], the basis
functions {i }n
i=1 are viewed as a discrete probability distribution {pi }n
i=1, and the polynomial
reproducing conditions are the under-determined constraints. To regularize the ill-posed problem,
the maximum-entropy principle was used. In this paper, as a generalization, the Shannon–Jaynes
entropy functional and the MAXENT or minimum relative entropy principle [16–18] is invoked to
obtain the basis functions. Sivia [44] presents an excellent introduction to Bayesian inference and
maximum-entropy methods, whereas Jaynes [50] provides a more rigorous and in-depth look at
probability theory from the Bayesian perspective.