Reviews: Scalable Levy Process Priors for Spectral Kernel Learning

The paper proposes a spectral mixture of laplacian kernel with a levy process prior on the spectral components. This extends on the SM kernel by Wilson, which is a mixture of gaussians with no prior on spectral components. A RJ-MCMC is proposed that can model the number of components and represent the spectral posterior. A large-scale approximation is also implemented (SKI). The idea of Levy prior on the spectral components is very interesting one, but the paper doesn't make it clear what are the benefits with respect to kernel learning.