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When can Expectation Maximization be used?
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.
What is the relationship between maximum likelihood estimation MLE and expectation maximization EM algorithm?
Maximum likelihood estimation is challenging on data in the presence of latent variables. Expectation maximization provides an iterative solution to maximum likelihood estimation with latent variables.
How is the expectation maximization algorithm used in statistics?
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.
When to use em to find maximum likelihood?
From what little I know the EM algorithm can be used to find the maximum likelihood when setting to zero the partial derivatives with respect to the parameters of the likelihood gives a set of equations that cannot be solved analytically.
When to use ordered subset expectation maximization ( EM )?
The EM algorithm (and its faster variant ordered subset expectation maximization) is also widely used in medical image reconstruction, especially in positron emission tomography, single-photon emission computed tomography, and x-ray computed tomography. See below for other faster variants of EM.
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.