Editing Maximum likelihood estimation (section)

=== Related concepts===
* [[Akaike information criterion]]: a criterion to compare statistical models, based on MLE 
* [[Extremum estimator]]: a more general class of estimators to which MLE belongs
* [[Fisher information]]: information matrix, its relationship to covariance matrix of ML estimates
* [[Mean squared error]]: a measure of how 'good' an estimator of a distributional parameter is (be it the maximum likelihood estimator or some other estimator)
* [[RANSAC]]: a method to estimate parameters of a mathematical model given data that contains [[outliers]]
* [[Rao–Blackwell theorem]]: yields a process for finding the best possible unbiased estimator (in the sense of having minimal [[mean squared error]]); the MLE is often a good starting place for the process
* [[Likelihood-ratio test#Asymptotic distribution: Wilks' theorem|Wilks' theorem]]: provides a means of estimating the size and shape of the region of roughly equally-probable estimates for the population's parameter values, using the information from a single sample, using a [[chi-squared distribution]]