Answers are grounded in this paper and cite their sources.
Theoretical analysis reveals sharp informational inequalities linking Rényi functionals and relative Fisher information, suggesting optimal bounds achieved by Gaussian and Beta distributions.
Several new and sharp informational inequalities are derived as a byproduct of Stam-like and moment-entropy-like inequalities in the relative framework and a recently established inequality mixing the Rényi entropy, the Rényi divergence and the Rényi cross-entropy of suitable probability density functions. More precisely, we obtain a Stam-like inequality connecting the Rényi entropy power, the recently introduced scaling-invariant relative Fisher information and the Rényi cross-entropy. Furthermore, we derive an inequality involving only Fisher-like informational measures and another inequality involving only moment-like functionals of non-relative, relative and cross types, respectively. All the inequalities are sharp. The minimizers of the Stam-like inequality are, in certain cases, pairs of Gaussian or stretched Gaussian probability densities; in contrast, each minimizer of the moment-like inequality is the probability density of the generalized Beta distribution.
No takes yet. Share an insight, caveat, or question.
Iagar et al. (2026) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: