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Neural Computation

August 15, 1998, Vol. 10, No. 6, Pages 1425-1433
(doi: 10.1162/089976698300017232)
© 1998 Massachusetts Institute of Technology
Bias/Variance Decompositions for Likelihood-Based Estimators
Article PDF (60.91 KB)
Abstract

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.