Name
Abstract | ||
|---|---|---|
A framework for deriving Rényi entropy-power inequalities (REPIs) is presented that uses linearization and an inequality of Dembo, Cover, and Thomas. Simple arguments are given to recover the previously known Rényi EPIs and derive new ones, by unifying a multiplicative form with constant c and a modification with exponent α of previous works. An information-theoretic proof of the Dembo-Cover-Thomas inequality-equivalent to Young's convolutional inequality with optimal constants-is provided, based on properties of Rényi conditional and relative entropies and using transportation arguments from Gaussian densities. For log-concave densities, a transportation proof of a sharp varentropy bound is presented.This work was partially presented at the 2019 Information Theory and Applications Workshop, San Diego, CA. |
Year | Venue | Keywords |
|---|---|---|
2020 | 2020 International Symposium on Information Theory and Its Applications (ISITA) | information-theoretic proof,Young's convolutional inequality,optimal constants,relative entropies,using transportation arguments,transportation proof,Rényi entropy power,normal transport,Rényi entropy-power inequalities,linearization,Dembo-Cover-Thomas inequality,Rényi EPIs,sharp varentropy bound,Gaussian densities |
DocType | ISSN | ISBN |
Conference | 2689-5838 | 978-1-7281-2855-9 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olivier Rioul | 1 | 92 | 23.54 |