2003•Unpublished venueRequires access

Maximum‐Likelihood Estimation

Simon Haykin

Open publisher page 4 citations

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.

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What this paper is about

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.

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Available 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.

Key concepts: Expectation–maximization algorithm, Maximum likelihood sequence estimation, Maximum likelihood, Likelihood function, Mathematics, Estimator, M-estimator, Restricted maximum likelihood

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