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Lecture
Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
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.