Extending integrated nested Laplace approximation to a class of near-Gaussian latent models. This work extends the integrated nested Laplace approximation (INLA) method to latent models outside the scope of latent Gaussian models, where independent components of the latent field can have a near-Gaussian distribution. The proposed methodology is an essential component of a bigger project that aims to extend the R package INLA in order to allow the user to add flexibility and challenge the Gaussian assumptions of some of the model components in a straightforward and intuitive way. Our approach is applied to two examples, and the results are compared with that obtained by Markov chain Monte Carlo, showing similar accuracy with only a small fraction of computational time. Implementation of the proposed extension is available in the R-INLA package.

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  1. Drori, Iddo: Deep variational inference (2020)
  2. Gianluca Baio: survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling (2020) not zbMATH
  3. Mejia, Amanda F.; Yue, Yu (Ryan); Bolin, David; Lindgren, Finn; Lindquist, Martin A.: A Bayesian general linear modeling approach to cortical surface fMRI data analysis (2020)
  4. Timothy D. Meehan, Nicole L. Michel, Håvard Rue: Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R (2020) not zbMATH
  5. Zammit-Mangion, Andrew; Rougier, Jonathan: Multi-scale process modelling and distributed computation for spatial data (2020)
  6. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  7. Daniel Turek, Mark Risser: Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R (2019) arXiv
  8. Dinsdale, Daniel; Salibian-Barrera, Matias: Modelling Ocean temperatures from bio-probes under preferential sampling (2019)
  9. Heaton, Matthew J.; Datta, Abhirup; Finley, Andrew O.; Furrer, Reinhard; Guinness, Joseph; Guhaniyogi, Rajarshi; Gerber, Florian; Gramacy, Robert B.; Hammerling, Dorit; Katzfuss, Matthias; Lindgren, Finn; Nychka, Douglas W.; Sun, Furong; Zammit-Mangion, Andrew: A case study competition among methods for analyzing large spatial data (2019)
  10. Khristenko, U.; Scarabosio, L.; Swierczynski, P.; Ullmann, E.; Wohlmuth, B.: Analysis of boundary effects on PDE-based sampling of Whittle-Matérn random fields (2019)
  11. McLean, M. W.; Wand, M. P.: Variational message passing for elaborate response regression models (2019)
  12. Sameh Abdulah, Yuxiao Li, Jian Cao, Hatem Ltaief, David E. Keyes, Marc G. Genton, Ying Sun: ExaGeoStatR: A Package for Large-Scale Geostatistics in R (2019) arXiv
  13. Tsokos, Alkeos; Narayanan, Santhosh; Kosmidis, Ioannis; Baio, Gianluca; Cucuringu, Mihai; Whitaker, Gavin; Király, Franz: Modeling outcomes of soccer matches (2019)
  14. Tufvesson, Oskar; Lindström, Johan; Lindström, Erik: Spatial statistical modelling of insurance risk: a spatial epidemiological approach to car insurance (2019)
  15. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  16. David Bolin; Finn Lindgren: Calculating Probabilistic Excursion Sets and Related Quantities Using excursions (2018) not zbMATH
  17. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018) not zbMATH
  18. Gómez-Rubio, Virgilio; Rue, Håvard: Markov chain Monte Carlo with the integrated nested Laplace approximation (2018)
  19. Jingyi Guo; Andrea Riebler: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice (2018) not zbMATH
  20. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018) not zbMATH

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