  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 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.
