Maximum Likelihood Estimators (MLE) are statistical methods used to estimate the parameters of a probability distribution. The idea is to find the parameter values that make the observed data most probable. By maximizing the likelihood function, which measures how likely the observed data is given certain parameters, MLE provides a way to infer the best-fitting parameters for a model. MLE is widely used in various fields, including economics, biology, and machine learning. It is particularly useful because it often leads to estimators with desirable properties, such as consistency and efficiency, making it a popular choice for statistical analysis.