Accounting for conceptual model uncertainty via maximum likelihood bayesian model averaging

Analyses of groundwater flow and transport typically rely on a single conceptual model of site hydrogeology. Yet hydrogeologic environments are open and complex, rendering them prone to multiple interpretations. Adopting only one of these may lead to statistical bias and underestimation of uncertainty. A comprehensive strategy for constructing alternative conceptual-mathematical models, selecting the best among them, and using them jointly to render optimum predictions under uncertainty is being developed by the author. This paper proposes a Maximum Likelihood Bayesian Model Averaging approach, MLBMA, to rendering optimum predictions by means of several competing models and assessing their joint predictive uncertainty.