GMM stands for Gaussian Mixture Model. It is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. GMMs are commonly used for clustering, density estimation, and as a component in more complex machine learning models. Each Gaussian distribution represents a cluster, and the model learns the parameters (mean, variance, and mixing probabilities) for each cluster to best fit the data.
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