Name
Title | ||
|---|---|---|
Extremal Results for the Relative-Entropy Ratio of Collinear Probability Distributions |
Abstract | ||
|---|---|---|
Tight upper and lower bounds on the ratio of relative entropies of two probability distributions with respect to a common third one were established in the authors' recent work (in Proc. 2018 IEEE Information Theory Workshop, Guangzhou, China, Nov. 2018), where the three distributions are collinear in the standard (n - 1)-simplex. In this paper, we establish some extremal results regarding these two bounds. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CWIT.2019.8929893 | 2019 16th Canadian Workshop on Information Theory (CWIT) |
Keywords | Field | DocType |
collinear probability distributions,upper bounds,lower bounds,relative entropies | Statistical physics,Information theory,Noise measurement,Upper and lower bounds,Probability distribution,Kullback–Leibler divergence,Mathematics | Conference |
ISBN | Citations | PageRank |
978-1-7281-0955-8 | 0 | 0.34 |
References | Authors | |
2 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengtian Yang | 1 | 2 | 0.77 |
| Jun Chen | 2 | 730 | 94.14 |