Maximum Likelihood Principle

A statistical decision principle that selects parameter values (or hypotheses) that maximize the likelihood function—the probability (or probability density) of the observed data given the parameterized model—i.e., θ̂ = argmax_θ L(θ; data). It requires an explicit probabilistic model for the data and treats likelihood as the objective function for point estimation.