Expectation Maximization Algorithm

Expectation Maximization Algorithm
Updated 19 Jan 2018

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This submission implements the Expectation Maximization algorithm and tests it on a simple 2D dataset.
The Expectation–Maximization (EM) algorithm is an iterative method to find 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 maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.

Github Repository:

Cite As

Reza Ahmadzadeh (2024). Expectation Maximization Algorithm (https://www.mathworks.com/matlabcentral/fileexchange/65772-expectation-maximization-algorithm), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2016a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes

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