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Bayesian‐information‐gap decision theory with an application to CO 2 sequestration
Author(s) -
O'Malley D.,
Vesselinov V. V.
Publication year - 2015
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2015wr017413
Subject(s) - bayesian probability , carbon sequestration , probabilistic logic , computer science , bayesian inference , decision theory , parametric statistics , sampling (signal processing) , operations research , engineering , mathematics , artificial intelligence , ecology , statistics , filter (signal processing) , carbon dioxide , computer vision , biology
Decisions related to subsurface engineering problems such as groundwater management, fossil fuel production, and geologic carbon sequestration are frequently challenging because of an overabundance of uncertainties (related to conceptualizations, parameters, observations, etc.). Because of the importance of these problems to agriculture, energy, and the climate (respectively), good decisions that are scientifically defensible must be made despite the uncertainties. We describe a general approach to making decisions for challenging problems such as these in the presence of severe uncertainties that combines probabilistic and nonprobabilistic methods. The approach uses Bayesian sampling to assess parametric uncertainty and Information‐Gap Decision Theory (IGDT) to address model inadequacy. The combined approach also resolves an issue that frequently arises when applying Bayesian methods to real‐world engineering problems related to the enumeration of possible outcomes. In the case of zero nonprobabilistic uncertainty, the method reduces to a Bayesian method. To illustrate the approach, we apply it to a site‐selection decision for geologic CO 2 sequestration.