  We study parametric Markov decision processes (PMDPs) and their reachability
probabilities "independent" of the parameters. Different to existing work on
parameter synthesis (implemented in the tools PARAM and PRISM), our main focus
is on describing different types of optimal deterministic memoryless schedulers
for the whole parameter range. We implement a simple prototype tool SEA-PARAM
that computes these optimal schedulers and show experimental results.
