Convergence of Statistical Estimators via Mutual Information Bounds
This work introduces a new mutual information bound to analyze how fast statistical estimators converge across different models. It bridges PAC-Bayesian theory and Bayesian nonparametrics, providing better convergence rates for fractional posteriors and tools for analyzing methods like variational inference or MLE.