Investigation shows varentropy measures uncertainty in continuous probability distributions, suggesting a solution to negative values in traditional methods.
In this work, we investigate the possibility of using varentropy, an information measure previously proposed for discrete probability distribution, as a measure of the probabilistic uncertainty of continuous probability distribution. We show that varentropy allows avoiding negative values and some undesirable features of informational entropy encountered while using the Boltzmann–Shannon formula (and others) for continuous probability distributions.
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Machu et al. (2026) studied this question.
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