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Maximum likelihood estimation

In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The point in the parameter space that maximizes the likelihood…

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Overview

Principles

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Non-independent variables

Iterative procedures

History

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Other estimation methods

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Maximum likelihood estimation

Nodes174
Edges173
Triples103
Avg. degree1.99
Density0.011494
Components1

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Maximum likelihood estimation

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related to Further reading · 57
Maximum likelihood estimation → Ahlquist, Amsterdam, An Introduction, Analysis, Andrew, Cambridge University Press, Cramer, Econometric Applications, Econometrics, Eliason, Gary, Hoboken, Hutchins, Inference, Introduction, ISBN, ISI Review, Jan, John, JSTOR
related to External links · 30
Maximum likelihood estimation → Andreas, Archived, Arne, College, El Paso, EMS Press, Encyclopedia, Henningsen, John, Lawrence, Lesser, Mathematical Sciences, Mathematics, Maximum, Maximum-likelihood, MLE, Ott, Purcell, Python, Quantitative Economics
related to Principles · 6
Maximum likelihood estimation → Euclidean, Evaluating, For, The, Theta, We
related to Properties · 4
Maximum likelihood estimation → As, However, If, Maximum-likelihood
has method · 3
Maximum likelihood estimation → Generalized, MAP, MLE
related to Nonparametric maximum likelihood estimation · 1
Maximum likelihood estimation → Nonparametric

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Important terminology

likelihood displaystyle maximum theta estimator function widehat distribution frac right left probability data mle parameters estimation mathbb parameter method sample

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
s in the place of 49 to represent the observed number of 'successes' of our Bernoulli trialsinstance of80.This result is easily generalized by substituting a letter0.80text
and a letter such as n in the place of 80 to represent the number of Bernoulli trialsinstance of80.This result is easily generalized by substituting a letter0.80text
Maximum likelihood estimationhas methodGeneralized0.60section
Maximum likelihood estimationhas methodMAP0.60section
Maximum likelihood estimationhas methodMLE0.60section
Maximum likelihood estimationrelated to External linksTilevik0.60section
Maximum likelihood estimationrelated to External linksAndreas0.60section
Maximum likelihood estimationrelated to External linksMaximum0.60section
Maximum likelihood estimationrelated to External linksMaximum-likelihood0.60section
Maximum likelihood estimationrelated to External linksEncyclopedia0.60section
Maximum likelihood estimationrelated to External linksMathematics0.60section
Maximum likelihood estimationrelated to External linksEMS Press0.60section

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