Monte Carlo Markov Chain (MCMC) sampling only the reionization history in an analytic framework [@Harker:2012p2856; @Morandi:2012p2857; @Patil:2014p2858]

Here, we improve on these approaches by developing a public 21 cm [EoR]{} analysis tool, named [<span style="font-variant:small-caps;">21CMMC</span>]{}. [<span style="font-variant:small-caps;">21CMMC</span>]{} uses an optimized version of the well-tested, public simulation tool, [<span style="font-variant:small-caps;">21CMFAST</span>]{} [@Mesinger:2007p122; @Mesinger:2011p1123], to generate 3D tomographic maps of the 21 cm brightness temperature, statistically comparing these to a (mock) observation. It quantifies the constraints and degeneracies of astrophysical parameters governing the EoR, within a full Bayesian MCMC framework.

The remainder of this paper is organized as follows. In Section \[sec:21CMMC\], we outline the development and implementation of [<span style="font-variant:small-caps;">21CMMC</span>]{}. In Section \[sec:SGP\], we highlight its performance at providing astrophysical constraints from a popular theoretical model of the [EoR]{} containing a single population of ionizing galaxies with a mass-independent ionizing efficiency. To further emphasize the strength and flexibility of [<span style="font-variant:small-caps;">21CMMC</span>]{} in Section \[sec:DPL\], we then estimate the recovery of astrophysical parameters from a more generalized [EoR]{} model containing two ionizing galaxy populations characterized by their mass-dependent ionizing efficiency. Finally, in Section \[sec:conclusion\] we summarize the performance of [<span style="font-variant:small-caps;">21CMMC</span>]{} and finish with our closing remarks. Throughout this work, we adopt the 