5.5 Summary¶
Likelihood is a fundamental concept in statistical inference. In this session we have introduced the definition of likelihood and demonstrated how it can be used to estimate an unknown parameter, through maximum likelihood estimation. For two examples, we have seen that estimates obtained by MLE are intuitively sensible for the parameter of interest and have derived them algebraically via the log-likelihood.
In the next session, we will find out about the specific mathematical properties which make the MLE a “good” estimator, and extend to the situation where our data consist of more than one observation.