Bayesian Inference: Gaussian Variables

This lecture covers Bayesian inference for jointly-distributed Gaussian random variables. It explains the joint Gaussian distribution, marginal and conditional pdfs, and the concept of uncorrelated Gaussian random vectors. The lecture also delves into the mean-square-error inference, Bayesian formulation, and maximum a-posteriori inference. It concludes with the Bayes classifier for binary classification problems.