Learning rate selection via weighted Fisher divergence

The general Bayesian approach provides a flexible framework by introducing a loss-based likelihood with a learning rate controlling the relative weight of the likelihood and prior. This flexibility creates an important calibration problem under model misspecification, where the conventional Bayesian