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Where is EM algorithm used?
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.
How do you explain EM algorithm?
The EM algorithm is an iterative approach that cycles between two modes. The first mode attempts to estimate the missing or latent variables, called the estimation-step or E-step. The second mode attempts to optimize the parameters of the model to best explain the data, called the maximization-step or M-step.
What is EM in data mining?
In data mining, expectation-maximization (EM) is generally used as a clustering algorithm (like k-means) for knowledge discovery. In statistics, the EM algorithm iterates and optimizes the likelihood of seeing observed data while estimating the parameters of a statistical model with unobserved variables.
What is an example of EM algorithm?
The EM Algorithm Ajit Singh November 20, 2005 1 Introduction Expectation-Maximization (EM) is a technique used in point estimation. Given a set of observable variables X and unknown (latent) variables Z we want to estimate parameters θ in a model. Example 1.1 (Binomial Mixture Model). You have two coins with unknown probabilities of
Does the EM algorithm always converge?
As you probably know, the EM algorithm has the property to increase the likelihood for each step. But that does not imply convergence. As an alternative to convergence to a local maximum, the sequence may approach the boundary of the parameter space, where the likelihood may be infinite.
What is the expectation maximization algorithm?
The Expectation-Maximization (EM) algorithm is a way to find maximum-likelihood estimates for model parameters when your data is incomplete, has missing data points, or has unobserved (hidden) latent variables. It is an iterative way to approximate the maximum likelihood function.