A learned Bayesian Cramér-Rao bound that estimates both prior and measurement distributions from data, via a new Fisher score matching objective, converges almost surely to the true bound under regularity conditions.
Phase noise in mimo systems: Bayesian Cram´er–Rao bounds and soft-input estimation,
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Learned Bayesian Cram\'er-Rao Bound for Unknown Measurement Models Using Score Neural Networks
A learned Bayesian Cramér-Rao bound that estimates both prior and measurement distributions from data, via a new Fisher score matching objective, converges almost surely to the true bound under regularity conditions.