Unlock: Maximum Likelihood Estimation: Theory, Information Identity, and Asymptotic Efficiency

MLE: pick the parameter that maximizes the likelihood of observed data. Score function, Bartlett identities, regularity conditions, consistency, asymptotic normality, Wilks' theorem, Cramér-Rao efficiency, exponential families, QMLE under misspecification, and the bridge to deep-learning negative log-likelihood training.