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连续分布的kullback-leibler散度估计Kullback-Leibler Divergence Estimation of Continuous Distributions |
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| 课程网址: | http://videolectures.net/ripd07_cruz_kld/ |
| 主讲教师: | Fernando Perez-Cruz |
| 开课单位: | 普林斯顿大学 |
| 开课时间: | 2008-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present a universal method for estimating the KL divergence between continuous densities and we prove it converges almost surely. Divergence estimation is typically solved estimating the densities first. Our main result shows this intermediate step is unnecessary and that the divergence can be either estimated using the empirical cdf or k-nearest-neighbour density estimation, which does not converge to the true measure for finite k. The convergence proof is based on describing the statistics of our estimator using waiting-times distributions, as the exponential or Erlang. We illustrate the proposed estimators and show how they compare to existing methods based on density estimation, and we also outline how our divergence estimators can be used for solving the two-sample problem. |
| 关 键 词: | 连续密度; KL偏差; 发散估计 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-14：lxf |
| 阅读次数: | 35 |