Maximum‐Likelihood Estimation
Simon Haykin
Abstract
Simon Haykin
Abstract
Abstract This article addresses different facets of maximum likelihood estimation. Starting with the likelihood function, the Cramér‐Rao inequality and properties of maximum likelihood estimators are described. Another estimator, namely, the conditional mean estimator, is discussed. An iterative algorithm, known as the Expectation‐Maximization (EM) algorithm, for computing the maximum likelihood estimate is described.
OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Abstract This article addresses different facets of maximum likelihood estimation. Starting with the likelihood function, the Cramér‐Rao inequality and properties of maximum likelihood estimators are described. Another estimator, namely, the conditional mean estimator, is discussed. An iterative algorithm, known as the Expectation‐Maximization (EM) algorithm, for computing the maximum likelihood estimate is described.
Key concepts: Expectation–maximization algorithm, Maximum likelihood sequence estimation, Maximum likelihood, Likelihood function, Mathematics, Estimator, M-estimator, Restricted maximum likelihood