Bayesian statistics can be applied to many problems that we encounter in classical statistics, such as the following:

• Parameter estimation
• Prediction
• Hypothesis testing
• Linear regression

There are many compelling reasons for studying Bayesian statistics, such as using prior information to better inform a current model. The Bayesian approach works with probability distributions rather than point estimates, thus producing more realistic predictions. Bayesian inference bases a hypothesis on the available data—P(hypothesis|data). The frequentist approach tries to fit the data based on a hypothesis. It can be argued that the Bayesian approach is the more logical and empirical one as it tries to base its belief on the facts rather than the other way round. For more information on this, refer to http://www.bayesian-inference.com/advantagesbayesian.