Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format
.Low-rank tensor approximations have shown great potential for uncertainty quantification in high dimensions, for example, to build surrogate models that can be used to speed up large-scale inference problems [M. Eigel, M. Marschall, and R. Schneider, Inverse Problems, 34 (2018), 035010; S. Dolgov et al., Stat. Comput., 30 (2020), pp. …