Lecture
This lecture covers the concept of estimation, focusing on measures of performance. It delves into finding functions to estimate unknown parameters, studying expectations with respect to the data, and deriving further results using log functions. The lecture also explores the Cauchy-Schwarz inequality, the Cramér-Rao lower bound, and the Fisher information. Additionally, it discusses the Rao-Blackwell theorem, sufficiency in estimation, and the maximum likelihood estimation method, providing examples for Bernoulli, Exponential, and Gaussian trials.