Operator-Adapted Wavelets, Fast Solvers, and Numerical Homogenization

This introduction reviews, summarizes, and illustrates fundamental connections between Bayesian inference, numerical quadrature, Gausssian process regression, polyharmonic splines, information-based complexity, optimal recovery, and game theory that form the basis of the book. This is followed by describing a sample of the results derived from these interplays, including those in numerical homogenization, operator-adapted wavelets, fast solvers, and Gaussian process regression. It finishes with an outline of the structure of the book.

• Publication year

  2019

• Venue

  Unknown venue

• Publication date

  2019-10-31

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1017/9781108594967
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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