# KRIGING IN TENSOR TRAIN DATA FORMAT

@article{Dolgov2019KRIGINGIT, title={KRIGING IN TENSOR TRAIN DATA FORMAT}, author={Sergey V. Dolgov and Alexander Litvinenko and Dishi Liu}, journal={Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)}, year={2019} }

Combination of low-tensor rank techniques and the Fast Fourier transform (FFT) based methods had turned out to be prominent in accelerating various statistical operations such as Kriging, computing conditional covariance, geostatistical optimal design, and others. However, the approximation of a full tensor by its low-rank format can be computationally formidable. In this work, we incorporate the robust Tensor Train (TT) approximation of covariance matrices and the efficient TT-Cross algorithm…

## 3 Citations

Iterative algorithms for the post-processing of high-dimensional data

- Computer ScienceJ. Comput. Phys.
- 2020

Computing f-Divergences and Distances of High-Dimensional Probability Density Functions - Low-Rank Tensor Approximations

- Computer ScienceArXiv
- 2021

The connection between the low-rank approximability of the d-dimensional random variable, its pcf , and its pdf is investigated, and it is shown how to go from the pcf or functional representation to the pdf, to reduce the computational complexity and storage cost from O(nd) to O(dnrα).

Tensorized low-rank circulant preconditioners for multilevel Toeplitz linear systems from high-dimensional fractional Riesz equations

- Computer ScienceComput. Math. Appl.
- 2022

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