ML Anthology

Computing Reference Classes

Ronald Prescott Loui
UAI 1986 pp. 273-290
doi:10.1016/B978-0-444-70396-5.50030-3 /uai/1986/loui1986uai-computing/

Abstract

For any system with limited statistical knowledge, the combination of evidence and the interpretation of sampling information require the determination of the right reference class (or of an adequate one). The present note (1) discusses the use of reference classes in evidential reasoning, and (2) discusses implementations of Kyburg's rules for reference classes. This paper contributes the first frank discussion of how much of Kyburg's system is needed to be powerful, how much can be computed effectively, and how much is philosophical fat.

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Text

Loui. "Computing Reference Classes." Conference on Uncertainty in Artificial Intelligence, 1986. doi:10.1016/B978-0-444-70396-5.50030-3

Markdown

[Loui. "Computing Reference Classes." Conference on Uncertainty in Artificial Intelligence, 1986.](https://mlanthology.org/uai/1986/loui1986uai-computing/) doi:10.1016/B978-0-444-70396-5.50030-3

BibTeX

@inproceedings{loui1986uai-computing,
  title     = {{Computing Reference Classes}},
  author    = {Loui, Ronald Prescott},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {1986},
  pages     = {273-290},
  doi       = {10.1016/B978-0-444-70396-5.50030-3},
  url       = {https://mlanthology.org/uai/1986/loui1986uai-computing/}
}