The prior distribution or prior is an assumed probability distribution that represents existing knowledge about a process. In other words, it is the probability distribution assumed in the absence of data about the problem.
It is a fundamental component of Bayes' theorem, where it is weighted by the Likelihood function of the statistical model's parameters to obtain the Posterior.
Priors can contain varying degrees of information: often, there is little prior knowledge about the problem at hand, in which case a generic, weakly informative prior is chosen. The simplest example is given by the principle of indifference, which states that, in the absence of relevant evidence about an event's distribution, the event can be assumed to follow a uniform distribution.