Paper detail

Analytical Forms for Most Likely Matrices Derived from Incomplete Information

Consider a rectangular matrix describing some type of communication or transportation between a set of origins and a set of destinations, or a classification of objects by two attributes. The problem is to infer the entries of the matrix from limited information in the form of constraints, generally the sums of the elements over various subsets of the matrix, such as rows, columns, etc, or from bounds on these sums, down to individual elements. Such problems are routinely addressed by applying the maximum entropy method to compute the matrix numerically, but in this paper we derive analytical, closed-form solutions. For the most complicated cases we consider the solution depends on the root of a non-linear equation, for which we provide an analytical approximation in the form of a power series. Some of our solutions extend to 3-dimensional matrices. Besides being valid for matrices of arbitrary size, the analytical solutions exhibit many of the appealing properties of maximum entropy, such as precise use of the available data, intuitive behavior with respect to changes in the constraints, and logical consistency.