Accounting for conceptual model uncertainty via maximum likelihood Bayesian model averaging

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Analyses of groundwater flow and transport typically rely on a single conceptual model of site hydrogeology. Yet hydrogeological 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.

Original languageEnglish (US)
Pages (from-to)303-313
Number of pages11
JournalIAHS-AISH Publication
Issue number277
StatePublished - Dec 1 2002

Keywords

  • Bayesian
  • Conceptual models
  • Maximum likelihood
  • Model uncertainty
  • Predictive uncertainty

ASJC Scopus subject areas

  • Water Science and Technology
  • Oceanography

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