Gaussian Mixture Model

A Gaussian Mixture Model assumes that data is generated not from a single distribution, but from multiple probabilistic components. It can be used for clustering, density estimation, and soft assignment problems. Its ability to provide the probability that each data point belongs to each cluster distinguishes it from hard-clustering methods. However, the distributional assumption and the number of components directly influence model quality.