maximum likelihood estimation

Maximum likelihood estimation is a method for estimating a model parameter from a set of data samples. It is based on the concept that the probability of obtaining a set of data samples is maximized by selecting a model parameter that maximizes the likelihood that the samples are associated with this specific parameter. To estimate the parameter, the likelihood of each set of data is evaluated, and the parameter that maximizes the likelihood of the data is chosen as the estimated parameter. The goal of maximum likelihood estimation is to find the parameter that best fits the data.