Name
Abstract | ||
|---|---|---|
A method for estimating Shannon differential entropy is proposed based on the second order expansion of the probability mass around the inspection point with respect to the distance from the point. Polynomial regression with Poisson error structure is utilized to estimate the values of density function. The density estimates at every given data points are averaged to obtain entropy estimators. The proposed estimator is shown to perform well through numerical experiments for various probability distributions. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46672-9_2 | ICONIP |
Keywords | Field | DocType |
Entropy,Regression,Density estimation,Poisson error structure | Applied mathematics,Polynomial regression,Maximum entropy thermodynamics,Artificial intelligence,Differential entropy,Joint entropy,Maximum entropy probability distribution,Entropy rate,Pattern recognition,Principle of maximum entropy,Statistics,Mathematics,Estimator | Conference |
Volume | ISSN | Citations |
9948 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hideitsu Hino | 1 | 99 | 25.73 |
| Shotaro Akaho | 2 | 650 | 79.46 |
| Noboru Murata | 3 | 855 | 170.36 |