Bayesian Inference: Part 2

This lecture delves into Bayesian inference, focusing on multiclass classification problems and the Bayes classifier. It covers the construction of the classifier, the minimization of erroneous decisions, and the probability of error. The lecture also explores logistic regression inference, loss functions, and the minimizer of logistic risk. Linear regression inference is discussed, emphasizing the mean-square-error problem and the estimation of random variables. The linear least-mean-square-error estimator and its optimization are detailed, along with the linear regression model and its implications.