Bayesian Cramér-Rao Bound Estimation with Score-Based Models

The Bayesian Cram\'er-Rao bound (CRB) provides a lower bound on the error of any Bayesian estimator under mild regularity conditions. It can be used to benchmark the performance of estimators, and provides a principled design metric for guiding system design and optimization. However, the Bayesian CRB depends on the prior distribution, which is often unknown for many problems of interest. This work develops a new data-driven estimator for the Bayesian CRB using score matching, a statistical estimation technique, to model the prior distribution. The performance of the estimator is analyzed in both the classical parametric modeling regime and the neural network modeling regime. In both settings, we develop novel non-asymptotic bounds on the score matching error and our Bayesian CRB estimator. Our proofs build on results from empirical process theory, including classical bounds and recently introduced techniques for characterizing neural networks, to address the challenges of bounding the score matching error. The performance of the estimator is illustrated empirically on a denoising problem example with a Gaussian mixture prior.