To cite INLA in publications please use (a subset of):

Havard Rue, Sara Martino, and Nicholas Chopin (2009), Approximate Bayesian Inference for Latent Gaussian Models Using Integrated Nested Laplace Approximations (with discussion), Journal of the Royal Statistical Society B, 71, 319-392.

Thiago G. Martins, Daniel Simpson, Finn Lindgren and Havard Rue (2013), Bayesian computing with INLA: New features, Computational Statistics and Data Analysis, 67(2013) 68-83

Finn Lindgren, Havard Rue, and Johan Lindstrom (2011). An Explicit Link Between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (with discussion), Journal of the Royal Statistical Society B, 73(4), 423-498.

Finn Lindgren, Havard Rue (2015). Bayesian Spatial Modelling with R-INLA. Journal of Statistical Software, 63(19), 1-25. URL http://www.jstatsoft.org/v63/i19/.

H. Rue, A. Riebler, S. H. Sorbye, J. B. Illian, D. P. Simpson, and F. K. Lindgren. Bayesian computing with INLA: A review. Annual Reviews of Statistics and Its Applications, 4(March):395-421, 2017. URL http://arxiv.org/abs/1604.00860

H. Bakka, H. Rue, G. A. Fuglstad, A. Riebler, D. Bolin, E. Krainski, D. Simpson, and F. Lindgren. Spatial modelling with R-INLA: A review. Invited extended review, arxiv:1802.06350, 2018.

A. De Coninck, B. De Baets, D. Kourounis, F. Verbosio, O. Schenk, S. Maenhout, and J. Fostier, Needles: Toward large-scale genomic prediction with marker-by-environment interaction, Genetics, vol. 203, no. 1, pp. 543-555, 2016.

F. Verbosio, A. D. Coninck, D. Kourounis, and O. Schenk, Enhancing the scalability of selected inversion factorization algorithms in genomic predictions, Journal of Computational Science, vol. 22, no. Supplement C, pp. 99-108, 2017.

D. Kourounis, A. Fuchs, and O. Schenk, Towards the next generation of multiperiod optimal power flow solvers, IEEE Transactions on Power Systems, vol. PP, no. 99, pp. 1-10, 2018.

Corresponding BibTeX entries:

  @Article{,
    author = {H{\aa}vard Rue and Sara Martino and Nicholas Chopin},
    title = {Approximate {Bayesian} Inference for Latent {Gaussian}
      Models Using Integrated Nested {Laplace} Approximations (with
      discussion).},
    journal = {Journal of the Royal Statistical Society B},
    year = {2009},
    volume = {71},
    pages = {319--392},
  }

  @Article{,
    title = {Bayesian computing with {INLA}: {N}ew features.},
    author = {Thiago G. Martins and Daniel Simpson and Finn Lindgren
      and H{\aa}vard Rue},
    year = {2013},
    volume = {67},
    pages = {68--83},
    journal = {Computational Statistics and Data Analysis},
  }

  @Article{,
    title = {An Explicit Link between {Gaussian} Fields and {Gaussian}
      {Markov} Random Fields: The Stochastic Partial Differential
      Equation Approach (with discussion).},
    author = {Finn Lindgren and H{\aa}vard Rue and Johan
      Lindstr{\"o}m},
    year = {2011},
    volume = {73},
    number = {4},
    pages = {423--498},
    journal = {Journal of the Royal Statistical Society B},
  }

  @Article{,
    title = {Bayesian Spatial Modelling with {R}-{INLA}},
    author = {Finn Lindgren and H{\aa}vard Rue},
    journal = {Journal of Statistical Software},
    year = {2015},
    volume = {63},
    number = {19},
    pages = {1--25},
    url = {http://www.jstatsoft.org/v63/i19/},
  }

  @Article{,
    title = {Bayesian computing with {INLA}: {A} review},
    author = {H{\aa}vard Rue and Andrea I. Riebler and Sigrunn H.
      S{\o}rbye and Janine B. Illian and Daniel P. Simpson and Finn K.
      Lindgren},
    journal = {Annual Reviews of Statistics and Its Applications},
    year = {2017},
    volume = {4},
    number = {March},
    pages = {395--421},
    url = {http://arxiv.org/abs/1604.00860},
  }

  @Article{,
    title = {Spatial modelling with {INLA}: {A} review},
    author = {Haakon Bakka and H{\aa}vard Rue and Geir-Arne Fuglstad
      and Andrea I. Riebler and David Bolin and Janine Illian and Elias
      Krainski and Daniel P. Simpson and Finn K. Lindgren},
    journal = {WIRES (Invited extended review)},
    year = {2018},
    volume = {xx},
    number = {Feb},
    pages = {xx--xx},
    url = {http://arxiv.org/abs/1802.06350},
  }

  @Article{,
    title = {Needles: Toward Large-Scale Genomic Prediction with
      Marker-by-Environment Interaction},
    author = {Arne {De Coninck} and Bernard {De Baets} and Drosos
      Kourounis and Fabio Verbosio and Olaf Schenk and Steven Maenhout
      and Jan Fostier},
    volume = {203},
    number = {1},
    pages = {543--555},
    year = {2016},
    doi = {10.1534/genetics.115.179887},
    journal = {Genetics},
    url = {http://dx.doi.org/10.1534/genetics.115.179887},
    eprint = {http://www.genetics.org/content/203/1/543.full.pdf},
  }

  @Article{,
    title = {Enhancing the scalability of selected inversion
      factorization algorithms in genomic prediction},
    author = {Fabio Verbosio and Arne {De Coninck} and Drosos Kourounis
      and Olaf Schenk},
    journal = {Journal of Computational Science},
    volume = {22},
    number = {Supplement C},
    pages = {99-108},
    year = {2017},
    issn = {1877-7503},
    url = {https://doi.org/10.1016/j.jocs.2017.08.013},
  }

  @Article{,
    title = {Towards the Next Generation of Multiperiod Optimal Power
      Flow Solvers},
    author = {D. Kourounis and A. Fuchs and O. Schenk},
    journal = {IEEE Transactions on Power Systems},
    year = {2018},
    volume = {PP},
    number = {99},
    pages = {1-10},
    url = {https://doi.org/10.1109/TPWRS.2017.2789187},
  }