Maximum Likelihood Estimation with Examples

Learn Maximum Likelihood Estimation (MLE), a fundamental method in statistical parameter estimation that serves as the foundation for Bayesian maximum a posteriori (MAP) estimation. Explore the problem statement and mathematical framework behind MLE, understanding how to derive estimators from first principles. Work through the complete derivation process, examining the theoretical underpinnings that make MLE such a powerful tool in statistics. Apply your knowledge through a detailed example involving MLE estimation of Poisson distribution parameters, seeing how the theory translates into practical problem-solving. Review key concepts and gain insights into how prior knowledge can be incorporated into the estimation process, bridging the gap between classical MLE and Bayesian approaches.