Application of the Dempster-Shafer theory of evidence for accident probability estimates

Jan Holmberg, Pekka Silvennoinen, Juhani Vira

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

Application of the Bayes' formula leaves little room for representation of ignorance and vagueness in quantitative estimates. Adhering to the classical probability calculus, the Bayesian approach can only replace ignorance with indifference. Shafer's belief functions are different in this respect. Free from the additivity requirement of classical probabilities, they preserve the vagueness of subjective beliefs. Together with Dempster's combination rule the belief functions offer an alternative to the Bayesian updating of probability estimates. In this paper the two methods are compared in a risk analysis application. While the results given by the Dempster-Shafer theory are, in essence, similar to those from the Bayesian analysis, the new method offers some presentational advantages for both the input and output data.
Original languageEnglish
Pages (from-to)47-58
JournalReliability Engineering and System Safety
Volume26
Issue number1
DOIs
Publication statusPublished - 1989
MoE publication typeA1 Journal article-refereed

Fingerprint

Accidents
Risk analysis

Cite this

@article{c4534eb11c93485b9238349d6445041e,
title = "Application of the Dempster-Shafer theory of evidence for accident probability estimates",
abstract = "Application of the Bayes' formula leaves little room for representation of ignorance and vagueness in quantitative estimates. Adhering to the classical probability calculus, the Bayesian approach can only replace ignorance with indifference. Shafer's belief functions are different in this respect. Free from the additivity requirement of classical probabilities, they preserve the vagueness of subjective beliefs. Together with Dempster's combination rule the belief functions offer an alternative to the Bayesian updating of probability estimates. In this paper the two methods are compared in a risk analysis application. While the results given by the Dempster-Shafer theory are, in essence, similar to those from the Bayesian analysis, the new method offers some presentational advantages for both the input and output data.",
author = "Jan Holmberg and Pekka Silvennoinen and Juhani Vira",
year = "1989",
doi = "10.1016/0951-8320(89)90083-5",
language = "English",
volume = "26",
pages = "47--58",
journal = "Reliability Engineering and System Safety",
issn = "0951-8320",
publisher = "Elsevier",
number = "1",

}

Application of the Dempster-Shafer theory of evidence for accident probability estimates. / Holmberg, Jan; Silvennoinen, Pekka; Vira, Juhani.

In: Reliability Engineering and System Safety, Vol. 26, No. 1, 1989, p. 47-58.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Application of the Dempster-Shafer theory of evidence for accident probability estimates

AU - Holmberg, Jan

AU - Silvennoinen, Pekka

AU - Vira, Juhani

PY - 1989

Y1 - 1989

N2 - Application of the Bayes' formula leaves little room for representation of ignorance and vagueness in quantitative estimates. Adhering to the classical probability calculus, the Bayesian approach can only replace ignorance with indifference. Shafer's belief functions are different in this respect. Free from the additivity requirement of classical probabilities, they preserve the vagueness of subjective beliefs. Together with Dempster's combination rule the belief functions offer an alternative to the Bayesian updating of probability estimates. In this paper the two methods are compared in a risk analysis application. While the results given by the Dempster-Shafer theory are, in essence, similar to those from the Bayesian analysis, the new method offers some presentational advantages for both the input and output data.

AB - Application of the Bayes' formula leaves little room for representation of ignorance and vagueness in quantitative estimates. Adhering to the classical probability calculus, the Bayesian approach can only replace ignorance with indifference. Shafer's belief functions are different in this respect. Free from the additivity requirement of classical probabilities, they preserve the vagueness of subjective beliefs. Together with Dempster's combination rule the belief functions offer an alternative to the Bayesian updating of probability estimates. In this paper the two methods are compared in a risk analysis application. While the results given by the Dempster-Shafer theory are, in essence, similar to those from the Bayesian analysis, the new method offers some presentational advantages for both the input and output data.

U2 - 10.1016/0951-8320(89)90083-5

DO - 10.1016/0951-8320(89)90083-5

M3 - Article

VL - 26

SP - 47

EP - 58

JO - Reliability Engineering and System Safety

JF - Reliability Engineering and System Safety

SN - 0951-8320

IS - 1

ER -