Scalable correlated inference using LatentKron
Created by: AnaryaRay1
For m1m2z inference, the GP's covariance matrix is a Kroneker product of individual matrices corresponding to m1m2 and z. We can thus exploit the fact that the Cholesky decomposition of a Kroneker product is the Kroneker product of individual Cholesky decompositions. This changes the computational scalability of the problem O(\prod_{\theta}n_{bins,\theta}^3)
to O(\sum_{\theta} n_{bins,\theta}^3)
. This is implemented by using Pymc GP's LatentKron instead of Latent.