MLE stands for Maximum Likelihood Estimation, which is a statistical method used to estimate the parameters of a statistical model by maximizing the likelihood function.
In other words, MLE is a technique that enables us to find the values of the model parameters that are most likely to produce the observed data. The likelihood function represents the probability of observing the given data under different parameter values.
MLE assumes that the data comes from a specified statistical distribution and provides an estimate of the parameters that best fit the data to that distribution. This estimation method is widely used in various fields, including economics, psychology, biology, and engineering.
The process of MLE involves formulating a mathematical model that represents the data distribution and its parameters. Then, the likelihood function is constructed using this model. By maximizing the likelihood function, the values of the parameters that would make the observed data the most likely to occur are determined.
MLE aims to find estimates that are not only close to the true values of the parameters but also have good statistical properties, such as efficiency and consistency. It is preferred over other estimation methods due to its simplicity and optimality in large samples.
In conclusion, Maximum Likelihood Estimation (MLE) is a statistical technique that finds the parameter values of a given model that are most likely to produce the observed data by maximizing the likelihood function.
