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The 55 pages that link to Maximum likelihood estimation, each with the reason it gives.

• Bayesian inferenceCompared with: It uses the likelihood alone, unlike Bayesian estimation’s combination of likelihood and prior.

• Bayes' theoremCompared with: Identical to Bayesian estimation when the prior is flat, showing precisely where priors matter.

• Bayesian statisticsCompared with: It uses the likelihood without combining it with a prior distribution.

• Prior probabilityCompared with: Unlike Bayesian estimation, it does not combine a likelihood with a prior distribution.

• Likelihood ratioCompared with: Estimation finds a best-fitting parameter; a likelihood ratio compares hypotheses or models.

• Bayesian phylogeneticsCompared with: It typically reports a best-fitting tree rather than a posterior distribution over trees.

• Long-branch attractionCompared with: Likelihood methods can outperform parsimony under rate heterogeneity when the model is suitable.

• Method of momentsCompared with: It uses the full likelihood rather than matching selected moments.

• Maximum a posteriori estimationCompared with: Unlike MAP, it does not incorporate a prior distribution.

• Gauss–Markov theoremCompared with: Unlike the theorem’s variance comparison, likelihood methods rely on a specified probability model.

• Lehmann–Scheffé theoremCompared with: A maximum-likelihood estimator need not be unbiased or minimum-variance among unbiased estimators.

• Sample mean and covarianceCompared with: For a normal model, its covariance estimate uses divisor n, unlike the common unbiased estimate’s n−1.