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Latent Score-Based Bayesian Cramér-Rao Bound Estimation for High-Dimensional Imaging Systems
We propose a data-driven framework for estimating the Bayesian Cramér-Rao bound (CRB) in high-dimensional imaging systems with complex, analytically intractable priors. Direct CRB computation is challenging in this setting due to the need to model the prior score and to form and invert the Bayesian
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