IAMCS Workshop in Large-Scale Inverse Problems and Uncertainty Quantification
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- Texas A&M University
- College Station, TX
- Stephen W. Hawking Auditorium
- George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy (MIST)
- Luis Tenorio, Colorado School of Mines
- Data Analysis Tools for Uncertainty Quantification of Inverse Problems
Abstract
We present exploratory data analysis methods to assess inversion estimates using examples based on 1^2- and 1^1-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: confidence intervals and bounds for the bias, resampling methods for model validation, and construction of training sets of functions with controlled local regularity.