Gaussian Mixture Regression: Examples and Analysis

This lecture covers the application of Gaussian Mixture Models (GMM) in regression problems, focusing on Gaussian Mixture Regression (GMR). The instructor explains how to draw the expected results of GMR on 2D distributions fitted with GMM, considering different priors and components. Various scenarios are discussed, such as the effect of using one or two components in GMR and the convergence of regression far away from components. The lecture also delves into the analytical expressions of GMR predictions, including linear regression forms and the impact of covariance matrices on the regression outcome.