ML Anthology

Jeffrey's Rule of Conditioning Generalized to Belief Functions

Philippe Smets
UAI 1993 pp. 500-505
/uai/1993/smets1993uai-jeffrey/

Abstract

Jeffrey's rule of conditioning has been proposed in order to revise a probability measure by another probability function. We generalize it within the framework of the models based on belief functions. We show that several forms of Jeffrey's conditionings can be defined that correspond to the geometrical rule of conditioning and to Dempster's rule of conditioning, respectively.

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Text

Smets. "Jeffrey's Rule of Conditioning Generalized to Belief Functions." Conference on Uncertainty in Artificial Intelligence, 1993.

Markdown

[Smets. "Jeffrey's Rule of Conditioning Generalized to Belief Functions." Conference on Uncertainty in Artificial Intelligence, 1993.](https://mlanthology.org/uai/1993/smets1993uai-jeffrey/)

BibTeX

@inproceedings{smets1993uai-jeffrey,
  title     = {{Jeffrey's Rule of Conditioning Generalized to Belief Functions}},
  author    = {Smets, Philippe},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {1993},
  pages     = {500-505},
  url       = {https://mlanthology.org/uai/1993/smets1993uai-jeffrey/}
}