R-INLA

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.


References in zbMATH (referenced in 37 articles , 3 standard articles )

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  1. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  2. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  3. David Bolin; Finn Lindgren: Calculating Probabilistic Excursion Sets and Related Quantities Using excursions (2018) not zbMATH
  4. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018) not zbMATH
  5. Gómez-Rubio, Virgilio; Rue, Håvard: Markov chain Monte Carlo with the integrated nested Laplace approximation (2018)
  6. Jingyi Guo; Andrea Riebler: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice (2018) not zbMATH
  7. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018) not zbMATH
  8. Suesse, Thomas: Estimation of spatial autoregressive models with measurement error for large data sets (2018)
  9. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018) not zbMATH
  10. Wang, Xiaofeng; Yue, Yu Ryan; Faraway, Julian J.: Bayesian regression modeling with INLA (2018)
  11. Zhou, Haiming; Hanson, Timothy: A unified framework for Fitting Bayesian semiparametric models to arbitrarily censored survival data, including spatially referenced data (2018)
  12. Andrew Zammit-Mangion, Noel Cressie: FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets (2017) arXiv
  13. Cortes, R. X.; Martins, T. G.; Prates, M. O.; Silva, B. A.: Inference on dynamic models for non-Gaussian random fields using INLA (2017)
  14. Dunlop, Matthew M.; Iglesias, Marco A.; Stuart, Andrew M.: Hierarchical Bayesian level set inversion (2017)
  15. Geppert, Leo N.; Ickstadt, Katja; Munteanu, Alexander; Quedenfeld, Jens; Sohler, Christian: Random projections for Bayesian regression (2017)
  16. Jouni Helske: KFAS: Exponential Family State Space Models in R (2017) not zbMATH
  17. Opitz, Thomas: Latent Gaussian modeling and INLA: a review with focus on space-time applications (2017)
  18. RESSTE Network; et al.: Analyzing spatio-temporal data with R: everything you always wanted to know -- but were afraid to ask (2017)
  19. Tobias Liboschik; Konstantinos Fokianos; Roland Fried: tscount: An R Package for Analysis of Count Time Series Following Generalized Linear Models (2017) not zbMATH
  20. Bradley, Jonathan R.; Cressie, Noel; Shi, Tao: A comparison of spatial predictors when datasets could be very large (2016)

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