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dc.contributor.authorBrown, Paul T.
dc.contributor.authorJoshi, Chaitanya
dc.contributor.authorJoe, Stephen
dc.contributor.authorRue, Haavard
dc.date.accessioned2020-11-16T05:35:43Z
dc.date.available2020-11-16T05:35:43Z
dc.date.issued2020-11-21
dc.date.submitted2019-11-22
dc.identifier.citationBrown, P. T., Joshi, C., Joe, S., & Rue, H. (2021). A novel method of marginalisation using low discrepancy sequences for integrated nested Laplace approximations. Computational Statistics & Data Analysis, 157, 107147. doi:10.1016/j.csda.2020.107147
dc.identifier.doi10.1016/j.csda.2020.107147
dc.identifier.urihttp://hdl.handle.net/10754/665955
dc.description.abstractRecently, it has been shown that approximations to marginal posterior distributions obtained using a low discrepancy sequence (LDS) can outperform standard grid-based methods with respect to both accuracy and computational efficiency. This recent method, which we will refer to as LDS-StM, can also produce good approximations to multimodal posteriors. However, implementation of LDS-StM into integrated nested Laplace approximations (INLA), a methodology in which grid-based methods are used, is challenging. Motivated by this problem, we propose modifications to LDS-StM that improves the approximations and make it compatible with INLA, without sacrificing computational speed. We also present two examples to demonstrate that LDS-StM with modifications can outperform INLA's own grid approximation with respect to speed and accuracy. We also demonstrate the flexibility of the new approach for the approximation of multimodal marginals.
dc.publisherElsevier BV
dc.relation.urlhttps://arxiv.org/pdf/1911.09880
dc.rightsArchived with thanks to Computational Statistics and Data Analysis
dc.titleA Novel Method of Marginalisation using Low Discrepancy Sequences for Integrated Nested Laplace Approximations
dc.typeArticle
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalComputational Statistics & Data Analysis
dc.rights.embargodate2022-11-12
dc.eprint.versionPost-print
dc.identifier.arxivid1911.09880
kaust.personRue, Haavard
dc.date.accepted2020-11-12
refterms.dateFOA2020-11-16T05:36:24Z
dc.date.published-online2020-11-21
dc.date.published-print2021-05
dc.date.posted2019-11-22


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