Maximum Likelihood Estimation

Maximum-likelihood (ML) estimation is a statistical method that aims to find the parameters of a statistical model that maximize the likelihood of observing the data. In other words, it seeks to find the values of the model parameters that make the observed data the most probable. The likelihood function measures how well the model fits the data, and maximizing it means finding the values of the parameters that make the observed data most likely to have been generated by the model. This approach is useful because it allows us to estimate the unknown parameters of a model based on the observed data, which is particularly important when we do not have prior knowledge of the parameter values.

Now, in mathematics, we always have to understand the definition of a term before we can proceed. In this case, we need to understand what is meant by the term “likelihood”.