Application of Parallel Hierarchical Matrices and Low-Rank Tensors in Spatial Statistics and Parameter Identification

Abstract
Part 1: Parallel H-matrices in spatial statistics

  1. Motivation: improve statistical model
  2. Tools: Hierarchical matrices
  3. Matern covariance function and joint Gaussian likelihood
  4. Identification of unknown parameters via maximizing Gaussian log-likelihood
  5. Implementation with HLIBPro. Part 2: Low-rank Tucker tensor methods in spatial statistics

    Acknowledgements
    KAUST

    Conference/Event Name
    SIAM Conference on Parallel Processing for Scientific Computing

    Additional Links
    https://www.siam.org/meetings/pp18/
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