Data analysis tools for uncertainty quantification of inverse problems

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Abstract

We present exploratory data analysis methods to assess inversion estimates using examples based on l(2)- and l(1)-regularization. These methods can be used to reveal the presence of systematic errors such as bias and discretization effects, or to validate assumptions made on the statistical model used in the analysis. The methods include bounds on the performance of randomized estimators of a large matrix, confidence intervals and bounds for the bias, resampling methods for model validation and construction of training sets of functions with controlled local regularity.

Detaljer

Författare
  • L. Tenorio
  • Fredrik Andersson
  • M. de Hoop
  • P. Ma
Enheter & grupper
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Matematik
Originalspråkengelska
Artikelnummer045001
TidskriftInverse Problems
Volym27
Utgåva nummer4
StatusPublished - 2011
PublikationskategoriForskning
Peer review utfördJa