Bernstein - von Mises theorems for statistical inverse problems I: Schrödinger equation

The inverse problem of determining the unknown potential $f>0$ in the partial differential equation $\frac{\Delta}{2} u - fu =0 \text{ on } \mathcal O ~~\text{s.t. } u = g \text { on } \partial \mathcal O,$ where $\mathcal O$ is a bounded $C^\infty$-domain in $\mathbb R^d$ and $g>0$ is a given function prescribing boundary values, is considered. The data consist of the solution $u$ corrupted by additive Gaussian noise. A nonparametric Bayesian prior for the function $f$ is devised and a Bernstein - von Mises theorem is proved which entails that the posterior distribution given the observations is approximated by an infinite-dimensional Gaussian measure that has a `minimal' covariance structure in an information-theoretic sense. The function space in which this approximation holds true is shown to carry the finest topology permitted for such a result to be possible. As a consequence the posterior distribution performs valid and optimal frequentist statistical inference on $f$ in the small noise limit.