To update the probability you assign to a prior after you make some observation you rescale it by P(observation | prior) / P(observation). This scaling factor is proportional to how well the prior predicted the observation and inversely proportional to how well the "average prior" predicted the observation.

So a prior's probability increases to the degree that it predicts an observation better than alternative priors.