Maximum likelihood estimation (MLE) is a statistical method that estimates the model parameters by finding values that are the most likely to have produced the observed data. It chooses the parameter values that maximize the likelihood function, which measures how likely the observed data are given particular parameter values. MLE is widely used in many areas of statistics, including regression, classification, and distribution fitting.
For example, suppose you want to estimate the average height of adult women in a population. You collect a random sample and assume the heights follow a normal distribution. MLE would find the values of the mean and standard deviation that are the most likely to produce the sample values you observed.
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