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

Chunlai Zhou Renmin University of China, Mingyue Wang Syracuse University, Biao Qin Renmin University of China
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:314-323, 2014.

Abstract

This paper studies the problem of revising belief- s using uncertain evidence in a framework where beliefs are represented by a belief function. We introduce two new Jeffrey’s rules for the revi- sion based on two forms of belief kinematics, an evidence-theoretic counterpart of probability kinematics. Furthermore, we provide two dis- tance measures for belief functions and show that the two belief kinematics are optimal in the sense that they minimize their corresponding distance measures.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-china14a, title = {Belief-Kinematics Jeffrey’s Rules in the Theory of Evidence}, author = {China, Chunlai Zhou Renmin University of and University, Mingyue Wang Syracuse and China, Biao Qin Renmin University of}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {314--323}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/china14a/china14a.pdf}, url = {https://proceedings.mlr.press/r12/china14a.html}, abstract = {This paper studies the problem of revising belief- s using uncertain evidence in a framework where beliefs are represented by a belief function. We introduce two new Jeffrey’s rules for the revi- sion based on two forms of belief kinematics, an evidence-theoretic counterpart of probability kinematics. Furthermore, we provide two dis- tance measures for belief functions and show that the two belief kinematics are optimal in the sense that they minimize their corresponding distance measures.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Belief-Kinematics Jeffrey’s Rules in the Theory of Evidence %A Chunlai Zhou Renmin University of China %A Mingyue Wang Syracuse University %A Biao Qin Renmin University of China %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-china14a %I PMLR %P 314--323 %U https://proceedings.mlr.press/r12/china14a.html %V R12 %X This paper studies the problem of revising belief- s using uncertain evidence in a framework where beliefs are represented by a belief function. We introduce two new Jeffrey’s rules for the revi- sion based on two forms of belief kinematics, an evidence-theoretic counterpart of probability kinematics. Furthermore, we provide two dis- tance measures for belief functions and show that the two belief kinematics are optimal in the sense that they minimize their corresponding distance measures. %Z Reissued by PMLR on 04 October 2026.
APA
China, C.Z.R.U.o., University, M.W.S. & China, B.Q.R.U.o.. (2014). Belief-Kinematics Jeffrey’s Rules in the Theory of Evidence. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:314-323 Available from https://proceedings.mlr.press/r12/china14a.html. Reissued by PMLR on 04 October 2026.

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