Bayesian Inference: Gaussian Prior for Mean

This lecture covers Bayesian inference for the mean of a Gaussian distribution, assuming the variance is known. It discusses the posterior mean, posterior variance, and the maximum a posteriori (MAP) estimator. The lecture also explains the concept of independence vs. conditional independence and the consecutive updates of the posterior distribution.