Name
Abstract | ||
|---|---|---|
In this paper, we present a novel entropy estimator for a given set of samples drawn from an unknown probability density function (PDF). Counter to other entropy estimators, the estimator presented here is parametric. The proposed estimator uses the maximum entropy principle to offer an to-term approximation to the underlying distribution and does not rely on local density estimation. The accuracy of the proposed algorithm is analyzed and it is shown that the estimation error is ≤ O(√(log n/n)). In addition to the analytic results, a numerical evaluation of the estimator on synthetic data as well as on experimental sensor network data is provided. We demonstrate a significant improvement in accuracy relative to other methods. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICASSP.2011.5946905 | ICASSP |
Keywords | Field | DocType |
synthetic data estimator,entropy estimation,sensor network data,approximation theory,probability density function,maximum entropy principle,computational complexity,maximum entropy methods,numerical evaluation,m-term approximation,maximum entropy,approximation algorithms,kernel,approximation error,synthetic data,entropy,sensor network,density estimation | Entropy estimation,Mathematical optimization,Maximum entropy spectral estimation,Joint entropy,Differential entropy,Principle of maximum entropy,Estimation theory,Mathematics,Estimator,Maximum entropy probability distribution | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4577-0537-3 | 978-1-4577-0537-3 | 4 |
PageRank | References | Authors |
0.48 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Behrouz Behmardi | 1 | 8 | 2.60 |
| Raviv Raich | 2 | 432 | 58.13 |
| Alfred O. Hero III | 3 | 2600 | 301.12 |