Inference in infinite‐dimensional inverse problems: Discretization and duality

Abstract

Many techniques for solving inverse problems involve approximating the unknown model, a function, by a finite‐dimensional “discretization” or parametric representation. The uncertainty in the computed solution is sometimes taken to be the uncertainty within the parametrization; this can result in unwarranted confidence. The theory of conjugate duality can overcome the limitations of discretization within the “strict bounds” formalism, a technique for constructing confidence intervals for functionals of the unknown model incorporating certain types of prior information. The usual computational approach to strict bounds approximates the “primal” problem in a way that the the resulting confidence intervals are at most long enough to have the nominal coverage probability. There is another approach based on “dual” optimization problems that gives confidence intervals with at least the nominal coverage probability. The pair of intervals derived by the two approaches bracket a correct confidence interval. The theory is illustrated with gravimetric, seismic, geomagnetic, and helioseismic problems and a numerical example in seismology.