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    A Novel Method of Marginalisation using Low Discrepancy Sequences for Integrated Nested Laplace Approximations

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    Articlefile1.pdf
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    1.726Mb
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    Description:
    Post-print
    Embargo End Date:
    2022-11-12
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    Type
    Article
    Authors
    Brown, Paul T.
    Joshi, Chaitanya
    Joe, Stephen
    Rue, Haavard cc
    KAUST Department
    Statistics Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-11-21
    Preprint Posting Date
    2019-11-22
    Online Publication Date
    2020-11-21
    Print Publication Date
    2021-05
    Embargo End Date
    2022-11-12
    Submitted Date
    2019-11-22
    Permanent link to this record
    http://hdl.handle.net/10754/665955
    
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    Abstract
    Recently, 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.
    Citation
    Brown, 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
    Publisher
    Elsevier BV
    Journal
    Computational Statistics & Data Analysis
    DOI
    10.1016/j.csda.2020.107147
    arXiv
    1911.09880
    Additional Links
    https://arxiv.org/pdf/1911.09880
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.csda.2020.107147
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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