Cramér-Rao Bound (CRB)

For parameter estimation problems, the Cramér-Rao Bound (CRB) establishes a fundamental lower limit on the variance of any unbiased estimator. This lower bound indicates that no unbiased estimator can achieve a variance smaller than the limit determined by CRB. Therefore, CRB serves as a crucial benchmark for evaluating the performance of unbiased estimators. This implementation provides a detailed computational framework for CRLB (Cramér-Rao Lower Bound) with configurable parameters, enabling users to adapt the calculation to their specific estimation scenarios. The code structure includes key functions for Fisher Information Matrix calculation and parameter sensitivity analysis, allowing researchers to test various statistical models and observe how different parameters affect the theoretical performance limits. We hope this program provides essential assistance in parameter estimation problems and stimulates further exploration and analytical thinking in statistical estimation theory.