1. ERDC Knowledge Core
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  3. Coastal and Hydraulics Laboratory (CHL) - (before 9/1981 - present)
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  5. Technical Report
Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/35753
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dc.contributor.authorSkahill, Brian E. (Brian Edward)-
dc.contributor.authorBaggett, Jeffrey S.-
dc.date.accessioned2020-03-04T18:30:18Z-
dc.date.available2020-03-04T18:30:18Z-
dc.date.issued2020-03-
dc.identifier.govdocERDC/CHL TR-20-2-
dc.identifier.urihttps://hdl.handle.net/11681/35753-
dc.identifier.urihttp://dx.doi.org/10.21079/11681/35753-
dc.descriptionTechnical Report-
dc.description.abstractMarkov chain Monte Carlo (MCMC) methods are widely used in hydrology and other fields for posterior inference in a Bayesian framework. A properly constructed MCMC sampler is guaranteed to converge to the correct limiting distribution, but convergence can be very slow. While most research is focused on improving the proposal distribution used to generate trial moves in the Markov chain, this work instead focuses on efficiently finding an initial population for population-based MCMC samplers that will expedite convergence. Four case studies, including two hydrological models, are used to demonstrate that using multi-level single linkage implicit filtering stochastic global optimization to initialize the population both reduces the overall computational cost and significantly increases the chance of finding the correct limiting distribution within the constraint of a fixed computational budget.en_US
dc.description.sponsorshipFlood and Coastal Systems Research and Development Program (U.S.)en_US
dc.description.tableofcontentsAbstract ................................ ................................ ................................ ................................ .... ii Figures and Tables ................................ ................................ ................................ .................. iv Preface ................................ ................................ ................................ ................................ ...... v 1 Introduction ................................ ................................ ................................ ...................... 1 1.1 Background ................................ ................................ ................................ ........ 1 1.2 ObjectiveObjective................................ ................................ ................................ ............. 2 1.3 Approach ................................ ................................ ................................ ............ 4 2 Methods ................................ ................................ ................................ ............................ 5 2.1 Implicit filtering (IMFIL) ................................ ................................ ...................... 5 2.2 MultiMulti-level single linkage (MLSL) ................................ ................................ ...... 5 2.3 Markov chain Monte Carlo (MCMC) ................................ ................................ .. 7 2.4 MCMCMCMC -MLSLMLSL-IMFIL ................................ ................................ ............................. 8 3 Examples and Results ................................ ................................ ................................ ..... 9 3.1 Bimodal normal target ................................ ................................ ...................... 9 3.2 TwentyTwenty-component mixture normal target ................................ ..................... 14 3.3 Hydrologic model applications ................................ ................................ ....... 28 4 Discussion and Conclusions ................................ ................................ ......................... 30 References ................................ ................................ ................................ ............................. 38 Acronyms and Abbreviations ................................ ................................ ............................... 42 Report Documentation Page-
dc.format.extent50 pages / 887.4 Kb-
dc.format.mediumPDF/A-
dc.language.isoen_USen_US
dc.publisherCoastal and Hydraulics Laboratory (U.S.)en_US
dc.publisherEngineer Research and Development Center (U.S.)-
dc.relation.ispartofseriesTechnical Report (Engineer Research and Development Center (U.S.)) ; no. ERDC/CHL TR-20-2-
dc.rightsApproved for Public Release; Distribution is Unlimited-
dc.sourceThis Digital Resource was created in Microsoft Word and Adobe Acrobat-
dc.subjectHydrologic modelsen_US
dc.subjectMarkov processesen_US
dc.subjectMathematical modelsen_US
dc.subjectMonte Carlo methoden_US
dc.subjectNumerical analysisen_US
dc.titleA practical two-phase approach to improve the reliability and efficiency of Markov chain Monte Carlo directed hydrologic model calibrationen_US
dc.typeReporten_US
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