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The principle of minimum cross-entropy (minimum directed divergence, minimum discrimination information) is a general method of inference about an unknown probability density when there exists a prior estimate of the density and new information in the form of constraints on expected values. Various fundamental properties of cross-entropy minimization are proven and collected in one place. Cross-entropy's well-known properties as an information measure are extended and strengthened when one of the densities involved is the result of cross-entropy minimization. The interplay between properties of cross-entropy minimization as an inference procedure and properties of cross-entropy as an information measure is pointed out. Examples are included and general analytic and computational methods of finding minimum cross-entropy probability densities are discussed.