MLE for Gaussian: EMV in Gaussian Model

This lecture covers the Maximum Likelihood Estimation (MLE) for the Gaussian mean and variance, discussing the calculation of MLE for mu and sigma^2, as well as the distribution of MLE for mu and sigma^2 when the data is Gaussian. The slides present formulas and derivations for MLE in the Gaussian model, highlighting the importance of parameter estimation in a Gaussian distribution. The lecture also explores the distribution of MLE for Gaussian data, emphasizing the characterization of MLE when assuming the data is truly Gaussian. Additionally, it touches on the stability of the Gaussian family and the consistency of MLE estimators for mean and variance. The content provides a detailed insight into the statistical estimation methods for Gaussian data.