Dependence in Random Vectors

This lecture covers the concept of dependence in random vectors, discussing joint frequency/density, conditional density, independence, and conditional independence. It also explores marginalization, transformations, covariance, correlation, and conditional expectation. The lecture delves into properties like linearity, unbiasedness, and the law of total variance, emphasizing the importance of understanding the covariance matrix and moment generating functions.