Chapter 6: Maximum Likelihood Estimation

Learning Objectives

• State the maximum likelihood principle and derive the score equation
• Prove consistency and asymptotic normality of the MLE under regularity conditions
• Apply the invariance principle to transform MLEs under reparameterization
• Compute MLEs in closed form for Gaussian, exponential, and linear-model problems
• Implement Newton-Raphson and Fisher scoring for iterative ML computation
• Identify the MLE structure behind the periodogram, matched filter, and DOA estimator