Expectation-Maximization for Gaussian Mixtures

Expectation-Maximization (EM) for Gaussian Mixtures is a statistical method used to find the best fit for data points that come from multiple overlapping groups, or "clusters," each represented by a Gaussian (bell-shaped) curve. The process has two main steps: in the "Expectation" step, it estimates the probability that each data point belongs to each cluster; in the "Maximization" step, it adjusts the parameters of the Gaussian curves based on these probabilities. This iterative cycle continues until the parameters stabilize, resulting in a clearer understanding of the underlying patterns in the data.