A Bayesian approach for consistent reconstruction of inclusions

Abstract

This paper considers a Bayesian approach for inclusion detection in nonlinear inverse problems using two known and popular push-forward prior distributions: the star-shaped and level set prior distributions. We analyze the convergence of the corresponding posterior distributions in a small measurement noise limit. The methodology is general; it works for priors arising from any Hölder continuous transformation of Gaussian random fields and is applicable to a range of inverse problems. 
 The level set and star-shaped prior distributions are examples of push-forward priors under Hölder continuous transformations that take advantage of the structure of inclusion detection problems. 
 We show that the corresponding posterior mean converges to the ground truth in a proper probabilistic sense. Numerical tests on a two-dimensional quantitative photoacoustic tomography problem showcase the approach. The results highlight the convergence properties of the posterior distributions and the ability of the methodology to detect inclusions with sufficiently regular boundaries.

Uncertainty quantification for goal-oriented inverse problems via variational encoder-decoder networks

“…We remark that for UQ applications, it is the number of independent samples that indicates the efficiency of an MCMC method. Obtaining a large number of independent samples from posteriors for level-set priors remains a challenge [51,52,61]. The MCMC method used for this problem yields approximately 10 independent samples according to figure 11(b).…”