Confidence intervals for functionals in constrained inverse problems via data-adaptive sampling-based calibration

We address functional uncertainty quantification for ill-posed inverse problems where it is possible to evaluate a possibly rank-deficient forward model, the observation noise distribution is known, and there are known parameter constraints.

We present four constraint-aware confidence intervals extending the work of Batlle et al.

(2023) by making the intervals both computationally feasible and less conservative.

Our approach first shrinks the potentially unbounded constraint set compact in a data-adaptive way, obtains samples of the relevant test statistic inside this set to estimate a quantile function, and then uses these computed quantities to produce the intervals.

Our data-adaptive bounding approach is based on the approach by Berger and Boos (1994), and involves defining a subset of the constraint set where the true parameter exists with high probability.

This probabilistic guarantee is then incorporated into the final coverage guarantee in the form of an uncertainty budget.

We then propose custom sampling algorithms to efficiently sample from this subset, even when the parameter space is high-dimensional.

Optimization-based interval methods formulate confidence interval computation as two endpoint optimizations, where the optimization constraints can be set to achieve different types of interval calibration while seamlessly incorporating parameter constraints.

However, choosing valid optimization constraints has been elusive.

We show that all four proposed intervals achieve nominal coverage for a particular functional both theoretically and in practice, with numerical examples demonstrating superior performance of our intervals over the OSB interval in terms of both coverage and expected length.

In particular, we show the superior performance in a realistic unfolding simulation from high-energy physics that is severely ill-posed and involves a rank-deficient forward model.