Beyond Hard Clusters: Probabilistic Modeling with Gaussian Mixtures

Gaussian Mixture Models extend traditional clustering by assigning probabilities rather than hard labels, making it possible to represent uncertainty when data points lie between clusters. Understanding GMMs and the EM algorithm is essential for practitioners working with unsupervised learning and probabilistic inference in machine learning. This practical guide demonstrates how to implement GMMs from scratch on real data, bridging the gap between theory and applied clustering.