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dc.contributor.authorKontoyiannis, I
dc.contributor.authorMertzanis, L
dc.contributor.authorPanotopoulou, A
dc.contributor.authorPapageorgiou, I
dc.contributor.authorSkoularidou, M
dc.date.accessioned2022-04-21T08:00:22Z
dc.date.available2022-04-21T08:00:22Z
dc.date.issued2022
dc.date.submitted2020-07-31
dc.identifier.issn1369-7412
dc.identifier.otherrssb12511
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/336305
dc.description.abstractWe develop a new Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series. We show that a version of the context tree weighting algorithm can compute the prior predictive likelihood exactly (averaged over both models and parameters), and two related algorithms are introduced, which identify the a posteriori most likely models and compute their exact posterior probabilities. All three algorithms are deterministic and have linear-time complexity. A family of variable-dimension Markov chain Monte Carlo samplers is also provided, facilitating further exploration of the posterior. The performance of the proposed methods in model selection, Markov order estimation and prediction is illustrated through simulation experiments and real-world applications with data from finance, genetics, neuroscience, and animal communication. The associated algorithms are implemented in the R package BCT.
dc.languageen
dc.publisherWiley
dc.subjectORIGINAL ARTICLE
dc.subjectORIGINAL ARTICLES
dc.subjectBayes factors
dc.subjectBayesian context tree
dc.subjectcontext tree weighting
dc.subjectdiscrete time series
dc.subjectexact Bayesian inference
dc.subjectMarkov chain Monte Carlo
dc.subjectMarkov order estimation
dc.subjectmodel selection
dc.subjectprediction
dc.titleBayesian context trees: Modelling and exact inference for discrete time series
dc.typeArticle
dc.date.updated2022-04-21T08:00:21Z
prism.publicationNameJournal of the Royal Statistical Society. Series B: Statistical Methodology
dc.identifier.doi10.17863/CAM.83723
dcterms.dateAccepted2022-03-04
rioxxterms.versionofrecord10.1111/rssb.12511
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.contributor.orcidKontoyiannis, I [0000-0001-7242-6375]
dc.identifier.eissn1467-9868
cam.issuedOnline2022-04-20


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