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George Apostolakis

Bio: George Apostolakis is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Probabilistic logic & Poison control. The author has an hindex of 24, co-authored 69 publications receiving 2207 citations.


Papers
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Journal ArticleDOI
07 Dec 1990-Science
TL;DR: The subjectivistic (Bayesian) theory of probability is the appropriate framework within which expert opinions, which are essential to the quantification process, can be combined with experimental results and statistical observations to produce quantitative measures of the risks from these systems.
Abstract: Safety assessments of technological systems, such as nuclear power plants, chemical process facilities, and hazardous waste repositories, require the investigation of the occurrence and consequences of rare events. The subjectivistic (Bayesian) theory of probability is the appropriate framework within which expert opinions, which are essential to the quantification process, can be combined with experimental results and statistical observations to produce quantitative measures of the risks from these systems. A distinction is made between uncertainties in physical models and state-of-knowledge uncertainties about the parameters and assumptions of these models. The proper role of past and future relative frequencies and several issues associated with elicitation and use of expert opinions are discussed.

566 citations

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TL;DR: In this paper, a review of the use of expert opinion in probabilistic risk assessment of nuclear power plants is presented, in light of the available empirical and theoretical results on expert opinion use.

146 citations

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TL;DR: In this paper, the impact of organizational factors on nuclear power plant safety can be determined by accounting for the dependence that these factors introduce among probabilistic safety assessment parameters via work processes.

134 citations

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TL;DR: In this paper, a method for estimating a probability distribution using estimates of its percentiles provided by experts is developed for estimating the conditional probability of equipment failure given a seismically induced stress.
Abstract: A method is developed for estimating a probability distribution using estimates of its percentiles provided by experts. The analyst's judgment concerning the credibility of these expert opinions is quantified in the likelihood function of Bayes’Theorem. The model considers explicitly the random variability of each expert estimate, the dependencies among the estimates of each expert, the dependencies among experts, and potential systematic biases. The relation between the results of the formal methods of this paper and methods used in practice is explored. A series of sensitivity studies provides insights into the significance of the parameters of the model. The methodology is applied to the problem of estimation of seismic fragility curves (i.e., the conditional probability of equipment failure given a seismically induced stress).

133 citations

Journal ArticleDOI
TL;DR: In this article, the work process analysis model-I (WPAM-I) along with its products developed in a previous paper (Davoudian, K., Wu, J.-S. & Apostolakis, G., Reliability Engineering and System Safety, 45 (1994) 85−105 are used as inputs to WPAM-II.

83 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, a quantitative definition of risk is suggested in terms of the idea of a "set of triplets" and extended to include uncertainty and completeness, and the use of Bayes' theorem is described in this connection.
Abstract: A quantitative definition of risk is suggested in terms of the idea of a “set of triplets”. The definition is extended to include uncertainty and completeness, and the use of Bayes' theorem is described in this connection. The definition is used to discuss the notions of “relative risk”, “relativity of risk”, and “acceptability of risk”.

2,808 citations

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TL;DR: It is explained how, in principle, early warning systems could be established to detect the proximity of some tipping points, and critically evaluate potential policy-relevant tipping elements in the climate system under anthropogenic forcing.
Abstract: The term "tipping point" commonly refers to a critical threshold at which a tiny perturbation can qualitatively alter the state or development of a system. Here we introduce the term "tipping element" to describe large-scale components of the Earth system that may pass a tipping point. We critically evaluate potential policy-relevant tipping elements in the climate system under anthropogenic forcing, drawing on the pertinent literature and a recent international workshop to compile a short list, and we assess where their tipping points lie. An expert elicitation is used to help rank their sensitivity to global warming and the uncertainty about the underlying physical mechanisms. Then we explain how, in principle, early warning systems could be established to detect the proximity of some tipping points.

2,660 citations

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TL;DR: This work develops methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models and provides a complete methodology for performing these analyses, in both deterministic and stochastic settings, and proposes novel techniques to handle problems encountered during these types of analyses.

2,014 citations

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TL;DR: The following techniques for uncertainty and sensitivity analysis are briefly summarized: Monte Carlo analysis, differential analysis, response surface methodology, Fourier amplitude sensitivity test, Sobol' variance decomposition, and fast probability integration.

1,780 citations

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TL;DR: Sampling-based methods for uncertainty and sensitivity analysis are reviewed and special attention is given to the determination of sensitivity analysis results.

1,179 citations