It is an iterative algorithm used to estimate the parameters of a Gaussian Mixture Models. The algorithm alternates between two steps:
- E-step: Calculates the posterior probabilities (responsibilities) of each data point belonging to each Gaussian component.
- M-step: Uses these responsibilities to re-calculate the GMM parameters (weights, means, and covariances).