ML Anthology

Bayesian Meta-Reasoning: Determining Model Adequacy from Within a Small World

Kathryn B. Laskey
UAI 1992 pp. 155-158
doi:10.1016/B978-1-4832-8287-9.50026-8 /uai/1992/laskey1992uai-bayesian/

Abstract

This paper presents a Bayesian framework for assessing the adequacy of a model without the necessity of explicitly enumerating a specific alternate model. A test statistic is developed for tracking the performance of the model across repeated problem instances. Asymptotic methods are used to derive an approximate distribution for the test statistic. When the model is rejected, the individual components of the test statistic can be used to guide search for an alternate model.

PDF UAI Semantic Scholar

Cite

Text

Laskey. "Bayesian Meta-Reasoning: Determining Model Adequacy from Within a Small World." Conference on Uncertainty in Artificial Intelligence, 1992. doi:10.1016/B978-1-4832-8287-9.50026-8

Markdown

[Laskey. "Bayesian Meta-Reasoning: Determining Model Adequacy from Within a Small World." Conference on Uncertainty in Artificial Intelligence, 1992.](https://mlanthology.org/uai/1992/laskey1992uai-bayesian/) doi:10.1016/B978-1-4832-8287-9.50026-8

BibTeX

@inproceedings{laskey1992uai-bayesian,
  title     = {{Bayesian Meta-Reasoning: Determining Model Adequacy from Within a Small World}},
  author    = {Laskey, Kathryn B.},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {1992},
  pages     = {155-158},
  doi       = {10.1016/B978-1-4832-8287-9.50026-8},
  url       = {https://mlanthology.org/uai/1992/laskey1992uai-bayesian/}
}