Bayesian inversion for electromyography using low-rank tensor formats

DOI10.1088/1361-6420/abd85azbMath1468.65179arXiv2009.02772OpenAlexW3083172993MaRDI QIDQ5859752

Tim A. Werthmann, Lars Grasedyck, Anna Rörich, Dominik Göddeke

Publication date: 20 April 2021

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2009.02772

zbMATH Keywords

inverse problemMetropolis-Hastings algorithmhierarchical Tucker formatEMGparameter-dependent problem

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Bayesian inference (62F15) Monte Carlo methods (65C05) Biological applications of optics and electromagnetic theory (78A70) Biomedical imaging and signal processing (92C55) Vector and tensor algebra, theory of invariants (15A72) Existence problems for PDEs: global existence, local existence, non-existence (35A01) Numerical methods for inverse problems for boundary value problems involving PDEs (65N21) Inverse problems (including inverse scattering) in optics and electromagnetic theory (78A46) Uniqueness problems for PDEs: global uniqueness, local uniqueness, non-uniqueness (35A02) Monte Carlo methods applied to problems in optics and electromagnetic theory (78M31)

Related Items (1)

State estimation in nonlinear parametric time dependent systems using tensor train

Uses Software

• htucker

Cites Work

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