Refer to PS2.3
a conditional distribution over the unobserved random variables, conditioned on the observed random variables, is called the posterior distribution. For instance p(θ|x, y) is the posterior distribution in the machine learning context.
A consequence of this approach is that, we are required to endow our model parameters, i.e. p(θ), with a prior distribution.
The prior probabilities are to be assigned before we see the data – they need to capture our prior beliefs of what the model parameters might be before observing any evidence, and must be a subjective opinion by the person building the model. ——— From PS2.3