In the 20th episode, we review the seminal paper by Rao which introduced the Cramer Rao bound:

Rao, Calyampudi Radakrishna (1945). "Information and the accuracy attainable in the estimation of statistical parameters". Bulletin of the Calcutta Mathematical Society. 37. Calcutta Mathematical Society: 81–89.

The Cramér-Rao Bound (CRB) sets a theoretical lower limit on the variance of any unbiased estimator for a parameter.

It is derived from the Fisher information, which quantifies how much the data tells us about the parameter. This bound provides a benchmark for assessing the precision of estimators and helps identify efficient estimators that achieve this minimum variance.

The CRB connects to key statistical concepts we have covered previously:

Consistency: Estimators approach the true parameter as the sample size grows, ensuring they become arbitrarily accurate in the limit. While consistency guarantees convergence, it does not necessarily imply the estimator achieves the CRB in finite samples.

Efficiency: An estimator is efficient if it reaches the CRB, minimizing variance while remaining unbiased. Efficiency represents the optimal use of data to achieve the smallest possible estimation error.

Sufficiency: Working with sufficient statistics ensures no loss of information about the parameter, increasing the chances of achieving the CRB. Additionally, the CRB relates to KL divergence, as Fisher information reflects the curvature of the likelihood function and the divergence between true and estimated distributions.

In modern DD and AI, the CRB plays a foundational role in uncertainty quantification, probabilistic modeling, and optimization. It informs the design of Bayesian inference systems, regularized estimators, and gradient-based methods like natural gradient descent. By highlighting the tradeoffs between bias, variance, and information, the CRB provides theoretical guidance for building efficient and robust machine learning models