Lecture
This lecture covers the concept of optimal Bayesian inference, focusing on denoising and scalar estimation. It delves into the posterior probability, Gaussian and Gauss-Bernoulli distributions, and the Bayes theorem. The lecture also explores the phase transition in the Curie-Weiss model and the spectrum of random matrices.