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Computing Statistical Moments Via Tensorization of Polynomial Chaos Expansions.

. SIAM/ASA J. Uncertain. Quantification, 12 (2): 289-308 (2024)

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The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks., and . CoRR, (2023)Sobol tensor trains for global sensitivity analysis., , and . Reliab. Eng. Syst. Saf., (2019)Tensor Methods for Global Sensitivity Analysis., , and . GI-Jahrestagung, volume P-294 of LNI, page 275-276. GI, (2019)Tensor Decompositions for Integral Histogram Compression and Look-Up., and . IEEE Trans. Vis. Comput. Graph., 25 (2): 1435-1446 (2019)Multiresolution Volume Filtering in the Tensor Compressed Domain., , and . IEEE Trans. Vis. Comput. Graph., 24 (10): 2714-2727 (2018)Compressing Bidirectional Texture Functions via Tensor Train Decomposition., and . PG (Short Papers), page 19-22. Eurographics Association, (2016)SGEMM GPU kernel performance., and . (February 2018)Computing Statistical Moments Via Tensorization of Polynomial Chaos Expansions.. SIAM/ASA J. Uncertain. Quantification, 12 (2): 289-308 (2024)A surrogate visualization model using the tensor train format., , and . SIGGRAPH Asia Symposium on Visualization, page 13:1-13:8. ACM, (2016)Tensor Algorithms for Advanced Sensitivity Metrics., , and . SIAM/ASA J. Uncertain. Quantification, 6 (3): 1172-1197 (2018)