Hurrah, we come to the end of the module! We started by learning about the difference between Frequentist and Bayesian Statistics. We then saw that Bayesian Statistics is an application of Conditional Probabiliy.

We understood what terms like Prior, Likelihood and Posterior meant. We also applied the Bayes' Theorem to a couple of scenarios.

In the next module, we will look at how Bayesian Statistics is implemented in practice. Up to now, we have considered Probability to be a function of only one variable. We will see what complications arise when the Probability becomes a function of multiple variables.