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Expectation–maximization algorithm

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…

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Introduction

Description

Properties

Proof of correctness

As a maximization–maximization procedure

Applications

Filtering and smoothing EM algorithms

Variants

Relation to variational Bayes methods

Geometric interpretation

Examples

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Expectation–maximization algorithm

Nodes100
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Avg. degree1.98
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Components1

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Important terminology

em algorithm displaystyle parameters theta boldsymbol mathbf latent likelihood log data step mid maximum expectation estimate maximization variables function values

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SubjectPredicateObjectConfidenceSrc
the Viterbi algorithm for hidden Markov modelsinstance ofor through an algorithm0.80text
those above are well studiedinstance ofThe convergence of parameter estimates0.80text
global convergence under certain conditions unlike EM which is often plagued by the issue of getting stuck in local optimainstance ofMoment-based approaches to learning the parameters of a probabilistic model enjoy guarantees0.80text
mixture modelsinstance ofAlgorithms with guarantees for learning can be derived for a number of important models0.80text
HMMs etcinstance ofAlgorithms with guarantees for learning can be derived for a number of important models0.80text
clustering using the soft k-means algorithminstance ofMacKay includes simple examples of the EM algorithm0.80text
and emphasizes the variational view of the EM algorithminstance ofMacKay includes simple examples of the EM algorithm0.80text
as described in Chapter 33.7 of version 7.2instance ofMacKay includes simple examples of the EM algorithm0.80text

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