Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization

1 code implementation • 30 Jan 2023 • Jian Cao, Myeongjong Kang, Felix Jimenez, Huiyan Sang, Florian Schafer, Matthias Katzfuss

To achieve scalable and accurate inference for latent Gaussian processes, we propose a variational approximation based on a family of Gaussian distributions whose covariance matrices have sparse inverse Cholesky (SIC) factors.

Gaussian Processes