Lecture

Multivariate Gaussian Laws

Description

This lecture revisits the previous week's topics, focusing on the multivariate Gaussian law. It covers interpreting probabilities based on averages, defining multivariate Gaussian laws, calculating probabilities using mean and covariance, and discussing independence and correlation in bivariate Gaussian laws. The lecture also delves into marginal and conditional laws in multivariate Gaussian laws, showcasing examples of height and weight distributions. The instructor explains how to find marginal laws, correlations, and conditional laws, emphasizing the importance of understanding the relationships between variables in multivariate Gaussian distributions.

Instructors (2)

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Official source

https://mediaspace.epfl.ch/media/0_nzr7ozfq

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Ontological neighbourhood

Mathematics

Probability theory: Topics in probability theory

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