  This article attempts to summarize the effort by the particle physics
community in addressing the tedious work of determining the parameter spaces of
beyond-the-standard-model (BSM) scenarios, allowed by data. These spaces,
typically associated with a large number of dimensions, especially in the
presence of nuisance parameters, suffer from the curse of dimensionality and
thus render naive sampling of any kind -- even the computationally inexpensive
ones -- ineffective. Over the years, various new sampling (from variations of
Markov Chain Monte Carlo (MCMC) to dynamic nested sampling) and machine
learning (ML) algorithms have been adopted by the community to alleviate this
issue. If not all, we discuss potentially the most important among them and the
significance of their results, in detail.
