Bias/variance decompositions for likelihood-based estimators

被引：70

作者：

Heskes, T ^{[1
]}

机构：

[1] Catholic Univ Nijmegen, Fdn Neural Networks, NL-6525 EZ Nijmegen, Netherlands

来源：

NEURAL COMPUTATION | 1998年 / 10卷 / 06期

关键词：

D O I：

10.1162/089976698300017232

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The bias/variance decomposition of mean-squared error is well understood and relatively straightforward. In this note, a similar simple decomposition is derived, valid for any kind of error measure that, when using the appropriate probability model, can be derived from a Kullback-Leibler divergence or log-likelihood.

引用

页码：1425 / 1433

页数：9