Brief

This post introduces multivariate probability models as a crucial concept in machine learning, moving beyond simpler univariate cases. It highlights the importance of understanding variable dependencies through concepts like covariance and correlation, and touches upon Simpson's Paradox. The discussion also covers the multivariate Gaussian distribution and the application of Mahalanobis distance for geometric insights into Gaussian density. AI