In Bayesian statistics, the marginal likelihood is used for model selection and averaging, yet it is often challenging to compute accurately for complex models. Approaches such as bridge sampling, while effective, suffer from high variance when the proposal distribution overlaps poorly with the target posterior. To quantify this variance, we present a closed-form Monte Carlo standard error (MCSE) estimator for bridge sampling, extending classical variance approximations with a multi-chain effective-sample-size correction for autocorrelated MCMC draws and an exact log-scale variance. We show that the MCSE estimate itself is structurally capped at about 1.05, so values near this cap signal saturation rather than precision, and our calibration experiments show that the MCSE can be trusted when it is below 0.3. Furthermore, we introduce a hybrid score-matching proposal that regularizes the sample covariance using the local posterior geometry, significantly improving the stability of the estimator, and we demonstrate the efficacy of these methods using increasingly difficult simulated posteriors and real posteriors from the posteriordb database.

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