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A new avenue for Bayesian inference with INLA

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  • Van Niekerk, Janet
  • Krainski, Elias
  • Rustand, Denis
  • Rue, Håvard

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.

Suggested Citation

  • Van Niekerk, Janet & Krainski, Elias & Rustand, Denis & Rue, Håvard, 2023. "A new avenue for Bayesian inference with INLA," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:csdana:v:181:y:2023:i:c:s0167947323000038
    DOI: 10.1016/j.csda.2023.107692
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    1. João Pedro Coli de Souza Monteneri Nacinben & Márcio Laurini, 2024. "Multivariate Stochastic Volatility Modeling via Integrated Nested Laplace Approximations: A Multifactor Extension," Econometrics, MDPI, vol. 12(1), pages 1-28, February.

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