Fast generation of isotropic Gaussian random fields on the sphere
  The efficient simulation of isotropic Gaussian random fields on the unit
sphere is a task encountered frequently in numerical applications. A fast
algorithm based on Markov properties and fast Fourier Transforms in 1d is
presented that generates samples on an n x n grid in O(n^2 log n). Furthermore,
an efficient method to set up the necessary conditional covariance matrices is
derived and simulations demonstrate the performance of the algorithm. An open
source implementation of the code has been made available at
https://github.com/pec27/smerfs .
