  We consider parametric Markov decision processes (pMDPs) that are augmented
with unknown probability distributions over parameter values. The problem is to
compute the probability to satisfy a temporal logic specification with any
concrete MDP that corresponds to a sample from these distributions. As solving
this problem precisely is infeasible, we resort to sampling techniques that
exploit the so-called scenario approach. Based on a finite number of samples of
the parameters, the proposed method yields high-confidence bounds on the
probability of satisfying the specification. The number of samples required to
obtain a high confidence on these bounds is independent of the number of states
and the number of random parameters. Experiments on a large set of benchmarks
show that several thousand samples suffice to obtain tight and high-confidence
lower and upper bounds on the satisfaction probability.
