Lecture
This lecture covers the concept of Bayes estimator, starting with modeling a variable as a random parameter with a priori distribution. It explains how to define the Bayes estimator based on the mean squared error and posterior distribution. The lecture also delves into the application of Bayes estimator in scenarios with quadratic cost, illustrating the estimation process using the maximum estimator. Additionally, it discusses the calculation of Bayes estimator for parameters in a test model, emphasizing the importance of probabilistic reasoning and decision rules in Bayesian inference.