学位论文详细信息
| Optimal entropy estimation on large alphabet: fundamental limits and fast algorithms | |
| entropy estimation;large alphabet | |
| Yang, Pengkun ; Wu ; Yihong | |
| 关键词: entropy estimation; large alphabet; | |
| Others : https://www.ideals.illinois.edu/bitstream/handle/2142/90776/YANG-THESIS-2016.pdf?sequence=1&isAllowed=y | |
| 美国|英语 | |
| 来源: The Illinois Digital Environment for Access to Learning and Scholarship | |
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【 摘 要 】
Consider the problem of estimating the Shannon entropy of a distributionover k elements from n independent samples. We obtain the minimax mean-square error within universal multiplicative constant factors if n exceeds aconstant factor of k/log(k); otherwise there exists no consistent estimator.This refines the recent result of Valiant and Valiant (2011) that the mini-mal sample size for consistent entropy estimation scales. The apparatus ofbest polynomial approximation plays a key role in both the construction ofoptimal estimators and, via a duality argument, the minimax lower bound.
【 预 览 】
| Files | Size | Format | View |
|---|---|---|---|
| Optimal entropy estimation on large alphabet: fundamental limits and fast algorithms | 427KB |
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