ML Anthology

Belief-Kinematics Jeffrey's Rules in the Theory of Evidence

Chunlai Zhou, Mingyue Wang, Biao Qin
UAI 2014 pp. 917-926
/uai/2014/zhou2014uai-belief/

Abstract

This paper studies the problem of revising beliefs using uncertain evidence in a framework where beliefs are represented by belief functions. We introduce two new Jeffrey's rules for belief revision by uncertain evidence in Dempster-Shafer theory, deriving them from a general principle of minimal commitment.

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Cite

Text

Zhou et al. "Belief-Kinematics Jeffrey's Rules in the Theory of Evidence." Conference on Uncertainty in Artificial Intelligence, 2014.

Markdown

[Zhou et al. "Belief-Kinematics Jeffrey's Rules in the Theory of Evidence." Conference on Uncertainty in Artificial Intelligence, 2014.](https://mlanthology.org/uai/2014/zhou2014uai-belief/)

BibTeX

@inproceedings{zhou2014uai-belief,
  title     = {{Belief-Kinematics Jeffrey's Rules in the Theory of Evidence}},
  author    = {Zhou, Chunlai and Wang, Mingyue and Qin, Biao},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {2014},
  pages     = {917-926},
  url       = {https://mlanthology.org/uai/2014/zhou2014uai-belief/}
}