Assessing parameter uncertainty on coupled models using minimum information methods

Tim Bedford

doi:10.1016/j.ress.2013.05.011

Assessing parameter uncertainty on coupled models using minimum information methods

2014-05-01

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DOI：10.1016/j.ress.2013.05.011

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Probabilistic inversion is used to take expert uncertainty assessments about observable model outputs and build from them a distribution on the model parameters that captures the uncertainty expressed by the experts. In this paper we look at ways to use minimum information methods to do this, focussing in particular on the problem of ensuring consistency between expert assessments about differing variables, either as outputs from a single model or potentially as outputs along a chain of models. The paper shows how such a problem can be structured and then illustrates the method with two examples; one involving failure rates of equipment in series systems and the other atmospheric dispersion and deposition. (C) 2013 Published by Elsevier Ltd.

Keyword:

Minimum information

Coupled models

Expert judgement

Probabilistic Risk Analysis

Gaussian plume

Assessing parameter uncertainty on coupled models using minimum information methods

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