Multivariate Normal Distribution

The multivariate normal distribution extends the concept of a bell-shaped curve to multiple variables simultaneously. Imagine measuring height, weight, and blood pressure of a group; these measurements often vary together in predictable ways. The distribution describes how likely different combinations of these measurements are, with most people falling near average values, and fewer at the extremes. It’s characterized by a center point (mean vector) and how the variables spread out and relate to each other (covariance). This model helps understand and analyze complex data where variables interact, providing a comprehensive picture of their joint behavior.