Overview
MLE is a fundamental method in frequentist statistics for estimating the parameters of a model. It finds the parameter values that make the observed data most probable.
How it Works
It constructs a Likelihood Function, which represents the probability of the observed data as a function of the model parameters. The MLE is the set of parameter values that maximizes this function.
Use Cases
- Estimating the coefficients in linear and logistic regression.
- Fitting probability distributions (e.g., Normal, Poisson) to data.