Abstract
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.
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References
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.
Bernardo M. J. & Smith M. F.A. Bayesian Theory. Wiley & Sons, Chichester, 1994.
Bigun, S. E. Risk Analysis of Catastrophes Using Bayes’ Methods. Research Report Department of Statistics, RRDS 1994A: 2, Stockhohn University.
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.
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.
Bigun, S. E. Risk analysis of major civil aircraft accidents in Europe. European Journal of Operational Research. 20th anniversary, 1995B, special issue.
Bigun, S. E. Bayesian Prediction Based on Few and Dependent Data. In progress.
French S. Updating of belief in the light of some else’s opinion. J. R. Statist. Soc. A 1980; 143: 43–48.
French S. Group consensus probability distributions: A critical survey. Bayesian statistics 2 1985; 183–202.
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.
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.
Huseby A.B. Combining opinions in a predictive case. Bayesian statistics 1988; 3: 641–651.
Kahneman D. & Slovic P. & Tversky A. Judgement under uncertainty: Heuristics and biases. Cambridge University press, Cambridge, 1982.
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.
Lindley V. D. The improvement of probability judgements. J. R. Statist. Soc. A, part 1, 1982; 145: 117–126.
Lindley V.D. Reconciliation of discrete probability distributions. Bayesian statistics 1985; 2: 375–390.
Lindley V. D & Singpurwalla. Reliability (and faultree analysis using expert opinions), ASAS 1986; 87-90.
Morris A. P. Decision analysis expert use. Management science 1974; 20: 1233–1241.
Morris A. P. Combining expert judgements: a Bayesian approach. Management science 1977; 22: 679–693.
Morris A. P. An axiomatic approach to expert resolution. Management science 1983; 29: 24–32.
Pörn K. On empirical Bayesian inference applied to poisson probability models. PhD thesis, Linköping University, No. 234, Linköping, 1990.
Pulkkinen U. Methods for combination of expert judgements. Reliability Engineering and system Safety 1993; 40: 111–118.
West M. Modelling expert opinion. Bayesian statistics 1988; 3: 493–508
Winkler R.L. Combining probability distributions from dependent information sources. Management Science 1981; 27: 479–488.
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© 1996 Springer-Verlag London
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Bigun, E.S. (1996). Catastrophe Risk Analysis in Technical Systems Using Bayesian Statistics. In: Cacciabue, P.C., Papazoglou, I.A. (eds) Probabilistic Safety Assessment and Management ’96. Springer, London. https://doi.org/10.1007/978-1-4471-3409-1_80
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DOI: https://doi.org/10.1007/978-1-4471-3409-1_80
Publisher Name: Springer, London
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