Abrahamsen, P., V. Kvernelv, and D. Barker. 2018.
βSimulation Of Gaussian Random Fields Using The Fast Fourier Transform (Fft).β In, 2018:1β14. European Association of Geoscientists & Engineers.
Chan, Grace, and Andrew T.A. Wood. 1997.
βAlgorithm AS 312: An Algorithm for Simulating Stationary Gaussian Random Fields.β Journal of the Royal Statistical Society: Series C (Applied Statistics) 46 (1): 171β81.
Chan, G., and A. T. A. Wood. 1999.
βSimulation of Stationary Gaussian Vector Fields.β Statistics and Computing 9 (4): 265β68.
Davies, Tilman M., and David Bryant. 2013.
βOn Circulant Embedding for Gaussian Random Fields in R.β Journal of Statistical Software 55 (9).
Dietrich, C. R., and G. N. Newsam. 1993.
βA Fast and Exact Method for Multidimensional Gaussian Stochastic Simulations.β Water Resources Research 29 (8): 2861β69.
Durrande, Nicolas, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, and James Hensman. 2019.
βBanded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era.β In
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, 2780β89. PMLR.
Erhel, Jocelyne, Mestapha Oumouni, GΓ©raldine Pichot, and Franck Schoefs. n.d. βAnalysis of Continuous Spectral Method for Sampling Stationary Gaussian Random Fields,β 26.
Gilboa, E., Y. SaatΓ§i, and J. P. Cunningham. 2015.
βScaling Multidimensional Inference for Structured Gaussian Processes.β IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (2): 424β36.
Graham, Ivan G., Frances Y. Kuo, Dirk Nuyens, Rob Scheichl, and Ian H. Sloan. 2017a.
βAnalysis of Circulant Embedding Methods for Sampling Stationary Random Fields.β arXiv:1710.00751 [Math], October.
Guinness, Joseph, and Montserrat Fuentes. 2016.
βCirculant Embedding of Approximate Covariances for Inference From Gaussian Data on Large Lattices.β Journal of Computational and Graphical Statistics 26 (1): 88β97.
Haran, Murali. 2011.
βGaussian Random Field Models for Spatial Data.β In
Handbook of Markov Chain Monte Carlo, edited by Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng. Vol. 20116022. Chapman and Hall/CRC.
Lang, Annika, and JΓΌrgen Potthoff. 2011.
βFast Simulation of Gaussian Random Fields.β Monte Carlo Methods and Applications 17 (3).
Liu, Yang, Jingfa Li, Shuyu Sun, and Bo Yu. 2019.
βAdvances in Gaussian Random Field Generation: A Review.β Computational Geosciences 23 (5): 1011β47.
Powell, Catherine E. 2014. βGenerating Realisations of Stationary Gaussian Random Fields by Circulant Embedding.β Matrix 2 (2): 1.
Rue, Havard. 2001.
βFast Sampling of Gaussian Markov Random Fields.β Journal of the Royal Statistical Society. Series B (Statistical Methodology) 63 (2): 325β38.
Rue, HΓ₯vard, and Leonhard Held. 2005.
Gaussian Markov Random Fields: Theory and Applications. Monographs on Statistics and Applied Probability 104. Boca Raton: Chapman & Hall/CRC.
Teichmann, Jakob, and Karl-Gerald van den Boogaart. 2016.
βEfficient Simulation of Stationary Multivariate Gaussian Random Fields with Given Cross-Covariance.β Applied Mathematics 7 (17): 2183β94.
Whittle, P. 1954. βOn Stationary Processes in the Plane.β Biometrika 41 (3/4): 434β49.
Whittle, P. 1952.
βTests of Fit in Time Series.β Biometrika 39 (3-4): 309β18.
βββ. 1953a.
βThe Analysis of Multiple Stationary Time Series.β Journal of the Royal Statistical Society: Series B (Methodological) 15 (1): 125β39.
βββ. 1953b.
βEstimation and Information in Stationary Time Series.β Arkiv FΓΆr Matematik 2 (5): 423β34.
Whittle, Peter. 1952.
βSome Results in Time Series Analysis.β Scandinavian Actuarial Journal 1952 (1-2): 48β60.
Wilson, James T, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, and Marc Peter Deisenroth. 2021.
βPathwise Conditioning of Gaussian Processes.β Journal of Machine Learning Research 22 (105): 1β47.
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