Learning the Bayesian framework statistics
Bayesian statisticians, sometimes called ‘Bayesians’ base their approach to the Bayes theorem but also a variety of other approaches such as including prior data and calculating posterior probabilities for statistical estimates. Before learning about all these, lets learn about the Bayes theorem. Bayes theorem is about the conditional and joint probabilities.
Let’s consider that there are two events, event A and event B. We can consider two scenarios, scenario one, what is the probability of an event A occurring given that event B occurred. An example would be what is the probability that thunder will occur if it rains. We can also consider a reverse scenario, what is the probability it rains given that we hear thunder. These are the conditional probabilities of events and we can write them as P (A|B) in scenario one and P (B|A),
Bayes rule (based on Bayes theorem)
The Bayes theorem enables the calculation...
