Lecture
This lecture covers Bayesian inference for precision in the Gaussian model when the mean is known, using a Gamma prior. It discusses improper priors, subjective vs objective priors, and the difference between frequentist and Bayesian probabilities. The lecture also explores the concept of consistency in Bayesian statistics.