Maximum Likelihood Estimation

Maximum Likelihood Estimation (MLE), also known as maximum probability estimation, is a theoretical point estimation method. Its fundamental principle is that after randomly drawing n sets of sample observations from a population model, the most reasonable parameter estimator should maximize the probability of obtaining these n sample observations from the model. Unlike least squares estimation which aims to find parameters that best fit sample data, MLE focuses on probability maximization. Implementation typically involves defining a likelihood function and using optimization algorithms to find parameter values that maximize this function.