The first thing we will do is generalize the concept of bias. We will say that a coin with a bias of 1 will always land heads, one with a bias of 0 will always land tails, and one with a bias of 0.5 will land half of the time heads and half of the time tails. To represent the bias, we will use the  parameter, and to represent the total number of heads for a  number of tosses, we will use the  variable. According to Bayes' theorem (equation 1.4), we have to specify the prior, , and likelihood, , we will use. Let's start with the likelihood.