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dc.contributor.authorLu, Deng
dc.contributor.authorIorio, Maria De
dc.contributor.authorJasra, Ajay
dc.contributor.authorRosner, Gary L.
dc.date.accessioned2019-12-24T12:04:40Z
dc.date.available2019-12-24T12:04:40Z
dc.date.issued2019-08-12
dc.identifier.urihttp://hdl.handle.net/10754/660810
dc.description.abstractIn this article we consider Bayesian inference for partially observed Andersson-Madigan-Perlman (AMP) Gaussian chain graph (CG) models. Such models are of particular interest in applications such as biological networks and financial time series. The model itself features a variety of constraints which make both prior modeling and computational inference challenging. We develop a framework for the aforementioned challenges, using a sequential Monte Carlo (SMC) method for statistical inference. Our approach is illustrated on both simulated data as well as real case studies from university graduation rates and a pharmacokinetics study.
dc.publisherSubmitted to AIMS
dc.relation.urlhttps://arxiv.org/pdf/1908.04002
dc.rightsArchived with thanks to arXiv
dc.titleBayesian Inference for Latent Chain Graphs
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalSubmitted to Foundations of Data Science
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Statistics & Applied Probability, National University of Singapore, Singapore, 117546, SG
dc.contributor.institutionYale-NUS, Singapore, 138527, SG. & Department of Statistical Science, University College London, UK
dc.contributor.institutionOncology Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA
dc.identifier.arxividarXiv:1908.04002
dc.identifier.arxivid1908.04002
kaust.personJasra, Ajay
refterms.dateFOA2019-12-24T12:05:06Z


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