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| Titel |
DREAM(D): an adaptive Markov Chain Monte Carlo simulation algorithm to solve discrete, noncontinuous, and combinatorial posterior parameter estimation problems |
| VerfasserIn |
J. A. Vrugt, C. J. F. Braak |
| 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 ; 15, no. 12 ; Nr. 15, no. 12 (2011-12-13), S.3701-3713 |
| Datensatznummer |
250013051
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| Publikation (Nr.) |
 copernicus.org/hess-15-3701-2011.pdf |
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| Zusammenfassung |
| Formal and informal Bayesian approaches have found widespread implementation
and use in environmental modeling to summarize parameter and predictive
uncertainty. Successful implementation of these methods relies heavily on the
availability of efficient sampling methods that approximate, as closely and
consistently as possible the (evolving) posterior target distribution. Much
of this work has focused on continuous variables that can take on any value
within their prior defined ranges. Here, we introduce theory and concepts of
a discrete sampling method that resolves the parameter space at fixed points.
This new code, entitled DREAM(D) uses the recently developed DREAM
algorithm (Vrugt et al., 2008, 2009a, b) as its main building block but
implements two novel proposal distributions to help solve discrete and
combinatorial optimization problems. This novel MCMC sampler maintains
detailed balance and ergodicity, and is especially designed to resolve the
emerging class of optimal experimental design problems. Three different case
studies involving a Sudoku puzzle, soil water retention curve, and rainfall –
runoff model calibration problem are used to benchmark the performance of
DREAM(D). The theory and concepts developed herein can be easily
integrated into other (adaptive) MCMC algorithms. |
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