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 70 articles , 4 standard articles )

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  1. Castro-Camilo, Daniela; Mhalla, Linda; Opitz, Thomas: Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures (2021)
  2. Gressani, Oswaldo; Lambert, Philippe: Laplace approximations for fast Bayesian inference in generalized additive models based on P-splines (2021)
  3. van Niekerk, Janet; Bakka, Haakon; Rue, Håvard: Competing risks joint models using R-INLA (2021)
  4. Drori, Iddo: Deep variational inference (2020)
  5. Gianluca Baio: survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling (2020) not zbMATH
  6. Lázaro, E.; Armero, C.; Gómez-Rubio, V.: Approximate Bayesian inference for mixture cure models (2020)
  7. Lenzi, Amanda; Genton, Marc G.: Spatiotemporal probabilistic wind vector forecasting over Saudi Arabia (2020)
  8. 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)
  9. 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
  10. Zammit-Mangion, Andrew; Rougier, Jonathan: Multi-scale process modelling and distributed computation for spatial data (2020)
  11. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  12. Araki, Takamitsu; Akaho, Shotaro: Spatially multi-scale dynamic factor modeling via sparse estimation (2019)
  13. Castro-Camilo, Daniela; Huser, Raphaël; Rue, Håvard: A spliced gamma-generalized Pareto model for short-term extreme wind speed probabilistic forecasting (2019)
  14. Daniel Turek, Mark Risser: Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R (2019) arXiv
  15. Dinsdale, Daniel; Salibian-Barrera, Matias: Modelling Ocean temperatures from bio-probes under preferential sampling (2019)
  16. 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)
  17. Khristenko, U.; Scarabosio, L.; Swierczynski, P.; Ullmann, E.; Wohlmuth, B.: Analysis of boundary effects on PDE-based sampling of Whittle-Matérn random fields (2019)
  18. Krainski, Elias T.; Gómez-Rubio, Virgilio; Bakka, Haakon; Lenzi, Amanda; Castro-Camilo, Daniela; Simpson, Daniel; Lindgren, Finn; Rue, Håvard: Advanced spatial modeling with stochastic partial differential equations using R and INLA (2019)
  19. McLean, M. W.; Wand, M. P.: Variational message passing for elaborate response regression models (2019)
  20. 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

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