The Cramer-Rao lower bound (CRLB) is a fundamental concept in statistics and signal processing that relates to the accuracy of estimations. The CRLB is a theoretical lower limit that sets an absolute minimum on the variance of any unbiased estimator of a parameter. This means that the CRLB provides an upper bound on the best achievable estimation accuracy for the parameter in question. There are a number of different synonyms for the CRLB, including the Cramer-Rao inequality, the Fisher information inequality, and the inverse Fisher information matrix. These different terms all relate to the same fundamental concept of the CRLB, which is a key tool in statistical inference and estimation theory.