Likelihood

Likelihood may resemble probability, but it offers a different perspective. Probability usually asks how likely events are given fixed parameters, while likelihood asks which parameter values best explain the observed data. This distinction is central to statistical modeling and maximum likelihood estimation. In machine learning, many models learn parameters by increasing the likelihood of the observed data. In that sense, likelihood is the mathematical language of the question: “Which parameters explain the data best?”