When teaching probability, it is customary to give examples of coin tosses. Whether it is going to rain or not is more or less like a coin toss. If we have two possible outcomes, the binomial distribution is appropriate. This distribution requires two parameters: the probability and the sample size.
In statistics, there are two generally accepted approaches. In the frequentist approach, we measure the number of coin tosses and use that frequency for further analysis. Bayesian analysis is named after its founder the Reverend Thomas Bayes. The Bayesian approach is more incremental and requires a prior distribution, which is the distribution we assume before performing experiments. The posterior distribution is the distribution we are interested in and which we obtain after getting new data from experiments. Let's first have a look at the following equations:
(3.7) and (3.8) describe the probability mass function for the binomial distribution. (3.9) comes from an essay published...