Catastrophe Risk Analysis in Technical Systems Using Bayesian Statistics

  • Elizabeth Saers Bigun
Conference paper


In this paper we present risk analysis models which are built on expert assessments of future risks. These models calibrate and aggregate judgements of the experts in order to predict the future risks. The models are constructed by applying Bayesian statistics.


Unknown Variable Expert Judgement Future Risk Bayesian Statistic Expert Assessment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Apostolakis G. Expert Judgements in Probabilistic Safety assessment. Accelerated Life Testing and Experts’ Opinions in Reliability. Proceedings of the International School of Physics “Enrico Fermi”, 28 July–1 August 1986. Amsterdam, North-Holland, 116–131. Edited by Clarotti C. A. & Lindley D.V.Google Scholar
  2. 2.
    Bernardo M. J. & Smith M. F.A. Bayesian Theory. Wiley & Sons, Chichester, 1994.CrossRefMATHGoogle Scholar
  3. 3.
    Bigun, S. E. Risk Analysis of Catastrophes Using Bayes’ Methods. Research Report Department of Statistics, RRDS 1994A: 2, Stockhohn University.Google Scholar
  4. 4.
    Bigun, S. E. Risk Analysis of Catastrophes Using Bayes’ Methods I: Models which build on experts’ judgements. Research Report Department of Statistics, RRDS 1994B: 10, Stockholm University.Google Scholar
  5. 5.
    Bigun, S. E. Risk Analysis of Catastrophes Using Bayes’ Methods II: Results from the empirical studies. Research Report Department of Statistics, RRDS 1995A: 1, Stockhohn University.Google Scholar
  6. 6.
    Bigun, S. E. Risk analysis of major civil aircraft accidents in Europe. European Journal of Operational Research. 20th anniversary, 1995B, special issue.Google Scholar
  7. 7.
    Bigun, S. E. Bayesian Prediction Based on Few and Dependent Data. In progress.Google Scholar
  8. 8.
    French S. Updating of belief in the light of some else’s opinion. J. R. Statist. Soc. A 1980; 143: 43–48.MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    French S. Group consensus probability distributions: A critical survey. Bayesian statistics 2 1985; 183–202.MathSciNetGoogle Scholar
  10. 10.
    Gåsemyr J. & Natvig B. Using expert opinions in Bayesian estimation of component 1 lifetimes in a shock model — a general predictive approach. Statistical Research Report, university of Oslo, No. 4, 1991.Google Scholar
  11. 11.
    Gåsemyr J. & Natvig B. Expert opinions in Bayesian estimation of system reliability in a shock model — the MTP connection. Statistical Research Report, university of Oslo, No. 2, 1992.Google Scholar
  12. 12.
    Huseby A.B. Combining opinions in a predictive case. Bayesian statistics 1988; 3: 641–651.MathSciNetGoogle Scholar
  13. 13.
    Kahneman D. & Slovic P. & Tversky A. Judgement under uncertainty: Heuristics and biases. Cambridge University press, Cambridge, 1982.Google Scholar
  14. 14.
    Lindley V. D & Tversky A. & Brown V. R. On the reconciliation of probability assessments. J. R. Statist. Soc. A, part 2, 1979; 142: 146–180.MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Lindley V. D. The improvement of probability judgements. J. R. Statist. Soc. A, part 1, 1982; 145: 117–126.MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Lindley V.D. Reconciliation of discrete probability distributions. Bayesian statistics 1985; 2: 375–390.Google Scholar
  17. 17.
    Lindley V. D & Singpurwalla. Reliability (and faultree analysis using expert opinions), ASAS 1986; 87-90.Google Scholar
  18. 18.
    Morris A. P. Decision analysis expert use. Management science 1974; 20: 1233–1241.CrossRefMATHGoogle Scholar
  19. 19.
    Morris A. P. Combining expert judgements: a Bayesian approach. Management science 1977; 22: 679–693.CrossRefGoogle Scholar
  20. 20.
    Morris A. P. An axiomatic approach to expert resolution. Management science 1983; 29: 24–32.CrossRefGoogle Scholar
  21. 21.
    Pörn K. On empirical Bayesian inference applied to poisson probability models. PhD thesis, Linköping University, No. 234, Linköping, 1990.Google Scholar
  22. 22.
    Pulkkinen U. Methods for combination of expert judgements. Reliability Engineering and system Safety 1993; 40: 111–118.CrossRefGoogle Scholar
  23. 23.
    West M. Modelling expert opinion. Bayesian statistics 1988; 3: 493–508Google Scholar
  24. 24.
    Winkler R.L. Combining probability distributions from dependent information sources. Management Science 1981; 27: 479–488.CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag London 1996

Authors and Affiliations

  • Elizabeth Saers Bigun
    • 1
  1. 1.Center for Safety Research, KTH, Royal Institute of Technology, and Department of StatisticsStockholm UniversityStockholmSweden

Personalised recommendations