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# What is Expectation Maximization algorithm in machine learning?

## What is Expectation Maximization algorithm in machine learning?

The expectation-maximization algorithm is an approach for performing maximum likelihood estimation in the presence of latent variables. It does this by first estimating the values for the latent variables, then optimizing the model, then repeating these two steps until convergence.

## What is Expectation Maximization algorithm used for explain it with example?

Introduction. The EM algorithm is used to find (local) maximum likelihood parameters of a statistical model in cases where the equations cannot be solved directly. Typically these models involve latent variables in addition to unknown parameters and known data observations.

## Is expectation maximization unsupervised learning?

Although EM is most useful in practice for lightly supervised data, it is more easily formulated for the case of unsupervised learning.

## How is the expectation maximization algorithm used in machine learning?

The Expectation-Maximization Algorithm, or EM algorithm for short, is an approach for maximum likelihood estimation in the presence of latent variables. A general technique for finding maximum likelihood estimators in latent variable models is the expectation-maximization (EM) algorithm. — Page 424, Pattern Recognition and Machine Learning, 2006.

## Which is the maximization step in the EM algorithm?

In the EM algorithm, the estimation-step would estimate a value for the process latent variable for each data point, and the maximization step would optimize the parameters of the probability distributions in an attempt to best capture the density of the data.

## How is expectation maximization used in structural engineering?

In structural engineering, the Structural Identification using Expectation Maximization (STRIDE) algorithm is an output-only method for identifying natural vibration properties of a structural system using sensor data (see Operational Modal Analysis ). EM is also used for data clustering.

## How is the gaussianmixture scikit-learn used in expectation maximization?

The GaussianMixture scikit-learn class can be used to model this problem and estimate the parameters of the distributions using the expectation-maximization algorithm. The class allows us to specify the suspected number of underlying processes used to generate the data via the n_components argument when defining the model.