Monte Carlo Methods

  • Douglas J. Crawford-Brown
Chapter

Abstract

The risk to individuals in a population is clouded by several issues that must be accounted for in risk-based decisions. These issues usually are treated in the risk characterization stage of a risk assessment:
  • Different individuals will experience different risks due to variability in the factors that control exposure, susceptibility and sensitivity. These differences arise from variation in location within the exposure field, variation in exposure factors (such as breathing rates or ingestion rates), variation in pharmacokinetic and pharmacodynamic properties, and variation in dose-response characteristics. The composite effect of these differences is that the risk to individuals in a population must be described by an intersubject variability probability density function rather than by a single estimate (often called a point estimate in risk assessment).

  • Neither the parameters appearing in models, nor the mathematical forms of the model, are known with complete accuracy. As a result, there is uncertainty in predictions of risk, either for an individual or population. This requires that risk estimates be characterized by an uncertainty probability density function describing the confidence associated with which each value of the risk.

Keywords

Monte Carlo Method Uncertainty Analysis Variability Distribution Quadratic Model Slope Factor 
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.

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References

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    Hoflman, F. and Gardner, R., “Evaluation of Uncertainties in Radiological Assessment Models”, in Radiological Assessment: A Textbook on Environmental Dose Analysis, ed. by Till, J. and Meyer, H., U.S. Nuclear Regulatory Commission, 1983.Google Scholar
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    J. Gentle, Random Number Generation and Monte Carlo Methods, Springer Verlag, New York, 1998.CrossRefGoogle Scholar
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    D. Crawford-Brown, Risk-Based Environmental Decisions: Methods and Culture, Kluwer Aademic Publishers, Boston, 1999.CrossRefGoogle Scholar
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    D. Crawford-Brown and J. Arnold, “Theory Testing, Evidential Reason and the Roleof Data in the Formation of Rational Confidence Concerning Risk”, Comparative Environmental Risk Assessment, ed. by C. Cothern, Lewis Publishers, 1992.Google Scholar
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    S. Toulmin, Foresight and Understanding, Hutchinson, London, 1961.Google Scholar
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    J. Bernardo and A. Smith, Bayesian Theory, John Wiley and Sons, New York, 2000.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Douglas J. Crawford-Brown
    • 1
  1. 1.University of North CarolinaChapel HillUSA

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