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dc.contributor.authorHermann, Simone-
dc.date.accessioned2016-06-07T11:49:58Z-
dc.date.available2016-06-07T11:49:58Z-
dc.date.issued2016-
dc.identifier.urihttp://hdl.handle.net/2003/35066-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17114-
dc.description.abstractIn many applications, stochastic processes are used for modeling. Bayesian analysis is a strong tool for inference as well as for prediction. We here present an R package for a large class of models, all based on the definition of a jump diffusion with a non-homogeneous Poisson process. Special cases, as the Poisson process itself, a general diffusion process or a hierarchical (mixed) diffusion model, are considered. The package is a general tool box, because it is based on the stochastic differential equation, approximated with the Euler scheme. Functions for simulation, estimation and prediction are provided for each considered model.en
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;28, 2016en
dc.subjectBayesian estimationen
dc.subjectstochastic differential equationen
dc.subject(jump) diffusionen
dc.subjecthidden Markov modelen
dc.subjecthierarchical (mixed) modelen
dc.subjectEuler-Maruyama approximationen
dc.subject.ddc310-
dc.subject.ddc330-
dc.subject.ddc620-
dc.titleBaPreStoPro: an R package for Bayesian prediction of stochastic processesen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 823

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