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| Titel |
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation |
| VerfasserIn |
M. Sadegh, J. A. Vrugt |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 12 ; Nr. 17, no. 12 (2013-12-05), S.4831-4850 |
| Datensatznummer |
250086016
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| Publikation (Nr.) |
 copernicus.org/hess-17-4831-2013.pdf |
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| Zusammenfassung |
| In recent years, a strong debate has emerged in the hydrologic literature regarding how
to properly treat nontraditional error residual distributions and quantify
parameter and predictive uncertainty. Particularly, there is strong
disagreement whether such uncertainty framework should have its roots within
a proper statistical (Bayesian) context using Markov chain Monte Carlo (MCMC)
simulation techniques, or whether such a framework should be based on a quite
different philosophy and implement informal likelihood functions and
simplistic search methods to summarize parameter and predictive
distributions. This paper is a follow-up of our previous work published in
Vrugt and Sadegh (2013) and demonstrates that approximate Bayesian computation (ABC)
bridges the gap between formal and informal statistical model–data fitting
approaches. The ABC methodology has recently emerged in the fields of biology
and population genetics and relaxes the need for an explicit likelihood
function in favor of one or multiple different summary statistics that
measure the distance of each model simulation to the data. This paper further
studies the theoretical and numerical equivalence of formal and informal
Bayesian approaches using discharge and forcing data from different
watersheds in the United States, in particular generalized likelihood uncertainty estimation (GLUE). We demonstrate that the limits of
acceptability approach of GLUE is a special variant of ABC if each discharge
observation of the calibration data set is used as a summary diagnostic. |
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