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Bayesian uncertainty assessment in multicompartment deterministic simulation models for environmental risk assessment
Author(s) -
Bates Samantha C.,
Cullen Alison,
Raftery Adrian E.
Publication year - 2003
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.590
Subject(s) - bayesian inference , computer science , bayesian probability , inference , uncertainty analysis , econometrics , artificial intelligence , mathematics , simulation
We use a special case of Bayesian melding to make inference from deterministic models while accounting for uncertainty in the inputs to the model. The method uses all available information, based on both data and expert knowledge, and extends current methods of ‘uncertainty analysis’ by updating models using available data. We extend the methodology for use with sequential multicompartment models. We present an application of these methods to deterministic models for concentration of polychlorinated biphenyl (PCB) in soil and vegetables. The results are posterior distributions of concentration in soil and vegetables which account for all available evidence and uncertainty. Model uncertainty is not considered. Copyright © 2003 John Wiley & Sons, Ltd.