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Bayesian Methods for Model Uncertainty Analysis with Application to Future Sea Level Rise

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  • Anand Patwardhan
  • Mitchell J. Small

Abstract

This paper addresses the use of data for identifying and characterizing uncertainties in model parameters and predictions. The Bayesian Monte Carlo method is formally presented and elaborated, and applied to the analysis of the uncertainty in a predictive model for global mean sea level change. The method uses observations of output variables, made with an assumed error structure, to determine a posterior distribution of model outputs. This is used to derive a posterior distribution for the model parameters. Results demonstrate the resolution of the uncertainty that is obtained as a result of the Bayesian analysis and also indicate the key contributors to the uncertainty in the sea level rise model. While the technique is illustrated with a simple, preliminary model, the analysis provides an iterative framework for model refinement. The methodology developed in this paper provides a mechanism for the incorporation of ongoing data collection and research in decision‐making for problems involving uncertain environmental change.

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  • Anand Patwardhan & Mitchell J. Small, 1992. "Bayesian Methods for Model Uncertainty Analysis with Application to Future Sea Level Rise," Risk Analysis, John Wiley & Sons, vol. 12(4), pages 513-523, December.
    • Handle: RePEc:wly:riskan:v:12:y:1992:i:4:p:513-523
    DOI: 10.1111/j.1539-6924.1992.tb00708.x
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    References listed on IDEAS

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    1. W. E. Vesely & D. M. Rasmuson, 1984. "Uncertainties in Nuclear Probabilistic Risk Analyses," Risk Analysis, John Wiley & Sons, vol. 4(4), pages 313-322, December.
    2. David C. Cox & Paul Baybutt, 1981. "Methods for Uncertainty Analysis: A Comparative Survey," Risk Analysis, John Wiley & Sons, vol. 1(4), pages 251-258, December.
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    2. Nicky J. Welton & A. E. Ades, 2005. "Estimation of Markov Chain Transition Probabilities and Rates from Fully and Partially Observed Data: Uncertainty Propagation, Evidence Synthesis, and Model Calibration," Medical Decision Making, , vol. 25(6), pages 633-645, November.
    3. Isabelle Albert & Emmanuel Grenier & Jean‐Baptiste Denis & Judith Rousseau, 2008. "Quantitative Risk Assessment from Farm to Fork and Beyond: A Global Bayesian Approach Concerning Food‐Borne Diseases," Risk Analysis, John Wiley & Sons, vol. 28(2), pages 557-571, April.
    4. Michela Catenacci & Carlo Giupponi, 2010. "Potentials and Limits of Bayesian Networks to Deal with Uncertainty in the Assessment of Climate Change Adaptation Policies," Working Papers 2010.7, Fondazione Eni Enrico Mattei.
    5. Michael S. Williams & Eric D. Ebel & David Vose, 2011. "Framework for Microbial Food‐Safety Risk Assessments Amenable to Bayesian Modeling," Risk Analysis, John Wiley & Sons, vol. 31(4), pages 548-565, April.
    6. Maxine E. Dakins & John E. Toll & Mitchell J. Small & Kevin P. Brand, 1996. "Risk‐Based Environmental Remediation: Bayesian Monte Carlo Analysis and the Expected Value of Sample Information," Risk Analysis, John Wiley & Sons, vol. 16(1), pages 67-79, February.
    7. Tschang, F. Ted & Dowlatabadi, Hadi, 1995. "A Bayesian technique for refining the uncertainty in global energy model forecasts," International Journal of Forecasting, Elsevier, vol. 11(1), pages 43-61, March.
    8. Kevin P. Brand & Mitchell J. Small, 1995. "Updating Uncertainty in an Integrated Risk Assessment: Conceptual Framework and Methods," Risk Analysis, John Wiley & Sons, vol. 15(6), pages 719-729, December.

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