Bias/variance decompositions for likelihood-based estimators

被引:68
作者
Heskes, T [1 ]
机构
[1] Catholic Univ Nijmegen, Fdn Neural Networks, NL-6525 EZ Nijmegen, Netherlands
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
收藏
页码:1425 / 1433
页数:9
相关论文
共 15 条