Fisher information

Summary

The Fisher information $I(\theta)$ measures how much an observation tells about an unknown parameter $\theta$ of a statistical model. It is the variance of the score, the derivative in $\theta$ of the log-likelihood. It bounds the precision of unbiased estimators through the Cramér–Rao bound, and its inverse is the asymptotic variance of the maximum likelihood estimator.