Maximum Likelihood

Should’ve paid more attention in class…

Simple terms, Maximum Likelihood is a way to choose model parameters so that the data you observed would be as probable as possible under the model

So if you decide your data follows a Normal distribution, you’re assuming your data looks like a bell curve described by 2 parameters:

• mean : the center of the bell
• standard deviation : how spread out it is

For proposed , the Normal distribution assigns a probability density to each data point. The likelihood measures how well these parameters explain all the datapoints together

The MLE then chooses and values that make the observed dataset fit the bell curve the best