The high level goals of the experiment are:

1. To understand the computation of likelihood of a class, given a sample.
2. To understand the the use of density/distribution functions to model a class.
3. To understand the effect of prior probabilities in Bayesian classification.
4. To understand how two (or more) density functions interact in the feature space to decide a decision boundary between classes.
5. To understand how the decision boundary varies based on nature of the density functions.