  Sup-normalized spectral functions form building blocks of max-stable and
Pareto processes and therefore play an important role in modeling spatial
extremes. For one of the most popular examples, the Brown-Resnick process,
simulation is not straightforward. In this paper, we generalize two approaches
for simulation via Markov Chain Monte Carlo methods and rejection sampling by
introducing new classes of proposal densities. In both cases, we provide an
optimal choice of the proposal density with respect to sampling efficiency. The
performance of the procedures is demonstrated in an example.
