  Statistical inference for extreme values of random events is difficult in
practice due to low sample sizes and inaccurate models for the studied rare
events. If prior knowledge for extreme values is available, Bayesian statistics
can be applied to reduce the sample complexity, but this requires a known
probability distribution. By working with the quantiles for extremely low
probabilities (in the order of $10^{-2}$ or lower) and relying on their
asymptotic normality, inference can be carried out without assuming any
distributions. Despite relying on asymptotic results, it is shown that a
Bayesian framework that incorporates prior information can reduce the number of
observations required to estimate a particular quantile to some level of
accuracy.
