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Using a parallelized MCMC algorithm in R to identify appropriate likelihood functions for SWAT

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dc.contributor.authorJoseph, J.F.
dc.contributor.authorGuillaume, Joseph
dc.date.accessioned2015-12-10T23:15:33Z
dc.date.issued2013
dc.date.updated2015-12-10T09:46:14Z
dc.description.abstractMarkov Chain Monte Carlo (MCMC) algorithms allow the analysis of parameter uncertainty. This analysis can inform the choice of appropriate likelihood functions, thereby advancing hydrologic modeling with improved parameter and quantity estimates and more
dc.identifier.issn1364-8152
dc.identifier.urihttp://hdl.handle.net/1885/64700
dc.publisherPergamon-Elsevier Ltd
dc.sourceEnvironmental Modelling and Software
dc.titleUsing a parallelized MCMC algorithm in R to identify appropriate likelihood functions for SWAT
dc.typeJournal article
local.bibliographicCitation.lastpage298
local.bibliographicCitation.startpage292
local.contributor.affiliationJoseph, J.F., The University of Texas at San Antonio
local.contributor.affiliationGuillaume, Joseph, College of Medicine, Biology and Environment, ANU
local.contributor.authoruidGuillaume, Joseph, u4220846
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor040608 - Surfacewater Hydrology
local.identifier.absfor010406 - Stochastic Analysis and Modelling
local.identifier.absseo960999 - Land and Water Management of environments not elsewhere classified
local.identifier.ariespublicationu4279067xPUB984
local.identifier.citationvolume46
local.identifier.doi10.1016/j.envsoft.2013.03.012
local.identifier.scopusID2-s2.0-84892491291
local.identifier.thomsonID000321088500027
local.type.statusPublished Version

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