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Article: Dynamic staged trees for discrete multivariate time series: forecasting, model selection and causal analysis

TitleDynamic staged trees for discrete multivariate time series: forecasting, model selection and causal analysis
Authors
KeywordsBayesian model selection
Dirichlet distribution
Graphical models
Bayes factors
Staged trees
Issue Date2011
PublisherInternational Society for Bayesian Analysis. The Journal's web site is located at http://ba.stat.cmu.edu/
Citation
Bayesian Analysis, 2011, v. 6 n. 2, p. 279-306 How to Cite?
AbstractA new tree-based graphical model - the dynamic staged tree - is proposed for modelling discrete-valued discrete-time multivariate processes which are hypothesised to exhibit symmetries in how some intermediate situations might unfold. We de-ne and implement a one-step-ahead prediction algorithm with the model using multi-process modelling and the power steady model that is robust to short-term variations in the data yet sensitive to underlying system changes. We demonstrate that the whole analysis can be performed in a conjugate way so that the potentially vast model space can be traversed quickly and then results communicated transparently. We also demonstrate how to analyse a general set of causal hypotheses on this model class. Our techniques are illustrated using a simple educational example. © 2011 International Society for Bayesian Analysis.
Persistent Identifierhttp://hdl.handle.net/10722/164798
ISSN
2015 Impact Factor: 1.087
2015 SCImago Journal Rankings: 1.559
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorFreeman, Gen_US
dc.contributor.authorSmith, JQen_US
dc.date.accessioned2012-09-20T08:09:42Z-
dc.date.available2012-09-20T08:09:42Z-
dc.date.issued2011en_US
dc.identifier.citationBayesian Analysis, 2011, v. 6 n. 2, p. 279-306en_US
dc.identifier.issn1931-6690-
dc.identifier.urihttp://hdl.handle.net/10722/164798-
dc.description.abstractA new tree-based graphical model - the dynamic staged tree - is proposed for modelling discrete-valued discrete-time multivariate processes which are hypothesised to exhibit symmetries in how some intermediate situations might unfold. We de-ne and implement a one-step-ahead prediction algorithm with the model using multi-process modelling and the power steady model that is robust to short-term variations in the data yet sensitive to underlying system changes. We demonstrate that the whole analysis can be performed in a conjugate way so that the potentially vast model space can be traversed quickly and then results communicated transparently. We also demonstrate how to analyse a general set of causal hypotheses on this model class. Our techniques are illustrated using a simple educational example. © 2011 International Society for Bayesian Analysis.-
dc.languageengen_US
dc.publisherInternational Society for Bayesian Analysis. The Journal's web site is located at http://ba.stat.cmu.edu/-
dc.relation.ispartofBayesian Analysisen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBayesian model selection-
dc.subjectDirichlet distribution-
dc.subjectGraphical models-
dc.subjectBayes factors-
dc.subjectStaged trees-
dc.titleDynamic staged trees for discrete multivariate time series: forecasting, model selection and causal analysisen_US
dc.typeArticleen_US
dc.identifier.emailFreeman, G: gfreeman@hku.hken_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1214/11-BA610-
dc.identifier.scopuseid_2-s2.0-79958694202-
dc.identifier.hkuros209017en_US
dc.identifier.volume6en_US
dc.identifier.issue2-
dc.identifier.spage279en_US
dc.identifier.epage306en_US
dc.identifier.isiWOS:000291435700008-
dc.publisher.placeUnited States-

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