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A Bayesian approach to analyzing uncertainty among flood frequency models
Author(s) -
Wood Eric F.,
RodríguezIturbe Ignacio
Publication year - 1975
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 - 10.1029/wr011i006p00839
Subject(s) - bayesian probability , probability distribution , uncertainty analysis , posterior probability , bayesian inference , bayesian statistics , sensitivity analysis , statistical model , bayesian hierarchical modeling , computer science , bayesian average , prior probability , bayesian linear regression , econometrics , statistics , mathematics
The statistical uncertainty resulting from the lack of knowledge of which model represents a given stochastic process is analyzed. This analysis of model uncertainty leads to a composite Bayesian distribution. The composite Bayesian distribution is a linear model of the individual Bayesian probability distributions of the individual models, weighted by the posterior probability that a particular model is the true model. The composite Bayesian probability model accounts for all sources of statistical uncertainty, both parameter uncertainty and model uncertainty. This model is the one that should be used in applied problems of decision analysis, for it best represents the knowledge, or lack of it, to the decision maker about future events of the process.