Maximum likelihood estimation

Maximum Likelihood Estimation

Maximum Likelihood Estimation (pronunciation: /ˈmæksɪməm ˈlaɪklɪhʊd ˌɛstɪˈmeɪʃən/), often abbreviated as MLE, is a method used in Statistics for estimating the parameters of a Statistical model. The term originates from the field of Probability theory, where "likelihood" refers to the probability of observing a given set of data given specific parameter values.

Etymology

The term "Maximum Likelihood Estimation" is derived from its method of operation. It seeks to find the parameter values that maximize the likelihood of making the observed data most probable. The concept was first introduced by the English statistician Ronald A. Fisher in the early 20th century.

Method

In Maximum Likelihood Estimation, the aim is to find the parameter values that make the observed data most probable. This is done by maximizing a likelihood function, hence the name. The likelihood function is a function of the parameters of a statistical model. MLE provides estimates for the model's parameters by finding the parameter values that maximize the likelihood function.

Related Terms

• Likelihood function
• Statistical model
• Parameters
• Probability theory
• Ronald A. Fisher

See Also

• Bayesian inference
• Method of moments (statistics)
• Least squares

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