ML Anthology

Bias/Variance Decompositions for Likelihood-Based Estimators

Tom Heskes
NeCo 1998 pp. 1425-1433
doi:10.1162/089976698300017232 /neco/1998/heskes1998neco-bias/

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.

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Text

Heskes. "Bias/Variance Decompositions for Likelihood-Based Estimators." Neural Computation, 1998. doi:10.1162/089976698300017232

Markdown

[Heskes. "Bias/Variance Decompositions for Likelihood-Based Estimators." Neural Computation, 1998.](https://mlanthology.org/neco/1998/heskes1998neco-bias/) doi:10.1162/089976698300017232

BibTeX

@article{heskes1998neco-bias,
  title     = {{Bias/Variance Decompositions for Likelihood-Based Estimators}},
  author    = {Heskes, Tom},
  journal   = {Neural Computation},
  year      = {1998},
  pages     = {1425-1433},
  doi       = {10.1162/089976698300017232},
  volume    = {10},
  url       = {https://mlanthology.org/neco/1998/heskes1998neco-bias/}
}