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A new avenue for Bayesian inference with INLA.

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Title: A new avenue for Bayesian inference with INLA.
Authors: Van Niekerk, Janet1 (AUTHOR) janet.vanniekerk@kaust.edu.sa, Krainski, Elias1 (AUTHOR), Rustand, Denis1 (AUTHOR), Rue, Håvard1 (AUTHOR)
Source: Computational Statistics & Data Analysis. May2023, Vol. 181, pN.PAG-N.PAG. 1p.
Subjects: Bayesian field theory, Big data, Proportional hazards models
Abstract: Integrated Nested Laplace Approximations (INLA) has been a successful approximate Bayesian inference framework since its proposal by Rue et al. (2009). The increased computational efficiency and accuracy when compared with sampling-based methods for Bayesian inference like MCMC methods, are some contributors to its success. Ongoing research in the INLA methodology and implementation thereof in the R package R-INLA , ensures continued relevance for practitioners and improved performance and applicability of INLA. The era of big data and some recent research developments, presents an opportunity to reformulate some aspects of the classic INLA formulation, to achieve even faster inference, improved numerical stability and scalability. The improvement is especially noticeable for data-rich models. Various examples of data-rich models, like Cox's proportional hazards model, an item-response theory model, a spatial model including prediction, and a three-dimensional model for fMRI data are used to illustrate the efficiency gains in a tangible manner. [ABSTRACT FROM AUTHOR]
Copyright of Computational Statistics & Data Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Integrated Nested Laplace Approximations (INLA) has been a successful approximate Bayesian inference framework since its proposal by Rue et al. (2009). The increased computational efficiency and accuracy when compared with sampling-based methods for Bayesian inference like MCMC methods, are some contributors to its success. Ongoing research in the INLA methodology and implementation thereof in the R package R-INLA , ensures continued relevance for practitioners and improved performance and applicability of INLA. The era of big data and some recent research developments, presents an opportunity to reformulate some aspects of the classic INLA formulation, to achieve even faster inference, improved numerical stability and scalability. The improvement is especially noticeable for data-rich models. Various examples of data-rich models, like Cox's proportional hazards model, an item-response theory model, a spatial model including prediction, and a three-dimensional model for fMRI data are used to illustrate the efficiency gains in a tangible manner. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computational Statistics & Data Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.csda.2023.107692
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Big data
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      – SubjectFull: Proportional hazards models
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      – TitleFull: A new avenue for Bayesian inference with INLA.
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              Text: May2023
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              Y: 2023
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