Statistical inference of the fundamental parameters of supersymmetric theories
is a challenging and active endeavor. Several sophisticated algorithms have been employed
to this end. While Markov-Chain Monte Carlo (MCMC) and nested sampling techniques
are geared towards Bayesian inference, they have also been used to estimate frequentist
confidence intervals based on the profile likelihood ratio. We investigate the performance
and appropriate configuration of MultiNest, a nested sampling based algorithm, when
used for profile likelihood-based analyses both on toy models and on the parameter space
of the Constrained MSSM. We find that while the standard configuration previously used
in the literarture is appropriate for an accurate reconstruction of the Bayesian posterior,
the profile likelihood is poorly approximated. We identify a more appropriate MultiNest
configuration for profile likelihood analyses, which gives an excellent exploration of the
profile likelihood (albeit at a larger computational cost), including the identification of the
global maximum likelihood value. We conclude that with the appropriate configuration
MultiNest is a suitable tool for profile likelihood studies, indicating previous claims to
the contrary are not well founded.