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dc.contributor.authorGiminez, O
dc.contributor.authorBonner, S J
dc.contributor.authorKing, Ruth
dc.contributor.authorParker, R A
dc.contributor.authorBrooks, S P
dc.contributor.authorJamieson, L E
dc.contributor.authorGrosbois, V
dc.contributor.authorMorgan, B J T
dc.contributor.authorThomas, Len
dc.contributor.editorThomson, D L
dc.contributor.editorCooch, E G
dc.contributor.editorConroy, M J
dc.coverage.spatial885-918en
dc.date.accessioned2009-04-22T12:46:39Z
dc.date.available2009-04-22T12:46:39Z
dc.date.issued2008
dc.identifier.citationModeling Demographic Processes in Marked Populations 885-918 2008en
dc.identifier.isbn978-0-387-78150-1en
dc.identifier.otherStAndrews.ResExp.Output.OutputID.16858en
dc.identifier.urihttp://hdl.handle.net/10023/677
dc.descriptionThis paper was presented at the EURING 2007 Technical Meeting, January 14-21, Dunedin, New Zealand. It has been submitted for publication in the conference proceedings, which will appear as a special issue of Environmental and Ecological Statistics.en
dc.descriptionThe zip file contains accompanying code in WinBUGSen
dc.description.abstractThe computer package WinBUGS is introduced. We first give a brief introduction to Bayesian theory and its implementation using Markov chain Monte Carlo (MCMC) algorithms. We then present three case studies showing how WinBUGS can be used when classical theory is difficult to implement. The first example uses data on white storks from Baden Württemberg, Germany, to demonstrate the use of mark-recapture models to estimate survival, and also how to cope with unexplained variance through random effects. Recent advances in methodology and also the WinBUGS software allow us to introduce (i) a flexible way of incorporating covariates using spline smoothing and (ii) a method to deal with missing values in covariates. The second example shows how to estimate population density while accounting for detectability, using distance sampling methods applied to a test dataset collected on a known population of wooden stakes. Finally, the third case study involves the use of state-space models of wildlife population dynamics to make inferences about density dependence in a North American duck species. Reversible Jump MCMC is used to calculate the probability of various candidate models. For all examples, data and WinBUGS code are provided.en
dc.format.extent29373 bytes
dc.format.extent548574 bytes
dc.format.extent2541 bytes
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoenen
dc.publisherSpringer Series: Environmental and Ecological Statisticsen
dc.relation.ispartofModeling Demographic Processes in Marked Populationsen
dc.rightsThe original publication is available at www.springerlink.comen
dc.subjectBayesian statisticsen
dc.subjectdensity dependenceen
dc.subjectdistance samplingen
dc.subjectexternal covariatesen
dc.subjecthierarchical modellingen
dc.subjectline transecten
dc.subjectmark-recaptureen
dc.subjectrandom effectsen
dc.subjectreversible jump MCMCen
dc.subjectspline smoothingen
dc.subjectstate-space modelen
dc.subjectsurvival estimationen
dc.subject.lccQen
dc.subject.lccQAen
dc.subject.lccQLen
dc.titleWinBUGS for population ecologists: Bayesian modeling using Markov Chain Monte Carlo methods.en
dc.typeBook itemen
dc.audience.mediatorSchool : Mathematics and Statisticsen
dc.audience.mediatorDepartment : Statisticsen
dc.description.versionPostprinten
dc.publicationstatusPublisheden
dc.statusPeer revieweden


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