Consistency of Maximum Likelihood Estimation

This lecture delves into the concept of consistency in maximum likelihood estimation (MLE). The instructor explores the mathematical reasoning behind the sensible estimators provided by likelihood and the guarantees they offer. By examining entropy and Kullback-Leibler divergence, the lecture aims to answer questions about the MLE's consistency and its approach to reasonable Mean Squared Error (MSE) performance.