This analysis reveals properties of discrete maximum-entropy distributions, suggesting distinctions through joint moments in modeling data.
A family of multivariate maximum‐entropy distributions with general discrete support is derived. Members of the family are distinguished from each other by different specified sets of joint non‐central moments. One of the members can be viewed as a discrete analogue of the continuous multivariate normal distribution. In an illustrative example, maximum likelihood estimation is used in fitting several bivariate discrete maximum‐entropy distributions to empirical data.
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David J. Hessen (2025) studied this question.
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