Expectation–maximization algorithm
id:
expectation-maximization-algorithm-183-13650417
title:
Expectation–maximization algorithm
text:
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters
brand slug:
wiki
category slug:
encyclopedia
description:
Iterative method for finding maximum likelihood estimates in statistical models
original url:
https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm
date created:
2004-02-15T18:39:48Z
date modified:
2024-09-07T07:06:28Z
main entity:
{"identifier":"Q1275153","url":"https://www.wikidata.org/entity/Q1275153"}
image:
{"content_url":"https://upload.wikimedia.org/wikipedia/commons/6/69/EM_Clustering_of_Old_Faithful_data.gif","width":360,"height":309}
fields total:
13
integrity:
16