convergence between the recovered 21 cm PS from the mock observation and the PS from the smaller 128$^{3}$ simulations. To account for the impact of shot noise on the smallest spatial modes (largest $k$) within these low-resolution simulations, we apply an additional cut at $k\geq1.0~{\rm Mpc}^{-1}$.

At each step in the MCMC algorithm we compute the 21 cm PS corresponding to that point in our astrophysical parameter space using the 3D realization from [<span style="font-variant:small-caps;">21CMFAST</span>]{}. We compute its likelihood using the $\chi^{2}$ statistic between $k=0.15$ and 1.0 Mpc$^{-1}$ to assess whether to accept or reject the new proposed set of [EoR]{} parameters.

Finally, we briefly discuss the improvements in our method to sample the astrophysical [EoR]{} parameter space, relative to previous works. These previous approaches have been restricted to either sampling a fixed, course grid of reionization models [e.g. @Barkana:2009p116; @Mesinger:2012p1131; @Zahn:2012p1156] or the fundamental assumption of Gaussian errors in Fisher Matrix applications (@Pober:2014p35; although new methods exist to overcome this assumption, e.g. @Joachimi:2011p3193 [@Sellentin:2014p3227]). Instead, with a Bayesian MCMC framework, we are able to more efficiently sample the [EoR]{} parameter space and by construction obtain direct estimates of the individual probability distribution functions (PDFs) of the [EoR]{} parameters, removing 