A Probabilistic Upper Bound on Differential Entropy
| dc.creator | DeStefano, Joseph | |
| dc.creator | Learned-Miller, Erik | |
| dc.date | 2005-04-21 | |
| dc.date.accessioned | 2026-07-07T08:15:26Z | |
| dc.date.available | 2026-07-07T08:15:26Z | |
| dc.description | A novel, non-trivial, probabilistic upper bound on the entropy of an unknown one-dimensional distribution, given the support of the distribution and a sample from that distribution, is presented. No knowledge beyond the support of the unknown distribution is required, nor is the distribution required to have a density. Previous distribution-free bounds on the cumulative distribution function of a random variable given a sample of that variable are used to construct the bound. A simple, fast, and intuitive algorithm for computing the entropy bound from a sample is provided. | |
| dc.identifier | https://arxiv.org/abs/cs/0504091 | |
| dc.identifier | http://arxiv.org/abs/cs/0504091 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/133447 | |
| dc.subject | Information Theory | |
| dc.title | A Probabilistic Upper Bound on Differential Entropy | |
| dc.type | text |