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    Joint Posterior Inference for Latent Gaussian Models with R-INLA

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    Type
    Preprint
    Authors
    Chiuchiolo, Cristian cc
    Niekerk, Janet van
    Rue, Haavard cc
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Statistics Program
    Date
    2021-12-06
    Permanent link to this record
    http://hdl.handle.net/10754/673943
    
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    Abstract
    Efficient Bayesian inference remains a computational challenge in hierarchical models. Simulation-based approaches such as Markov Chain Monte Carlo methods are still popular but have a large computational cost. When dealing with the large class of Latent Gaussian Models, the INLA methodology embedded in the R-INLA software provides accurate Bayesian inference by computing deterministic mixture representation to approximate the joint posterior, from which marginals are computed. The INLA approach has from the beginning been targeting to approximate univariate posteriors. In this paper we lay out the development foundation of the tools for also providing joint approximations for subsets of the latent field. These approximations inherit Gaussian copula structure and additionally provide corrections for skewness. The same idea is carried forward also to sampling from the mixture representation, which we now can adjust for skewness.
    Publisher
    arXiv
    arXiv
    2112.02861
    Additional Links
    https://arxiv.org/pdf/2112.02861.pdf
    Collections
    Preprints; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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