Maximum Likelihood Estimation

This lecture covers the problem of point estimation, focusing on maximum likelihood estimators and their relationship with Kullback-Leibler divergence. It discusses the asymptotic properties of the MLE, including consistency and asymptotic normality. The lecture also explores examples such as the geometric and uniform distributions, illustrating the properties of MLEs. The asymptotic theory for MLEs is presented, emphasizing the regularity conditions required for consistency. The lecture concludes with a discussion on the asymptotic distribution of the MLE and the critical aspect of proving its consistency.