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Although neural-network-based emulators enable efficient parameter estimationin 21-cm cosmology, the accuracy of such constraints is poorly understood. Weemploy nested sampling to fit mock data of the global 21-cm signal and high-$z$galaxy ultraviolet luminosity function (UVLF) and compare for the first timethe emulated posteriors obtained using the global signal emulator ${\ttglobalemu}$ to the `true' posteriors obtained using the full model on which theemulator is trained using ${\tt ARES}$. Of the eight model parameters weemploy, four control the star formation efficiency (SFE), and thus can beconstrained by UVLF data, while the remaining four control UV and X-ray photonproduction, and the minimum virial temperature of star-forming halos ($T_{\rmmin}$), and thus are uniquely probed by reionization and 21-cm measurements.For noise levels of 50 and 250 mK in the 21-cm data being jointly-fit, theemulated and `true' posteriors are consistent to within $1\sigma$. However, atlower noise levels of 10 and 25 mK, ${\tt globalemu}$ overpredicts $T_{\rmmin}$ and underpredicts $\gamma_{\rm lo}$, an SFE parameter, by$\approx3-4\sigma$, while the `true' ${\tt ARES}$ posteriors capture theirfiducial values within $1\sigma$. We find that jointly-fitting the mock UVLFand 21-cm data significantly improves constraints on the SFE parameters bybreaking degeneracies in the ${\tt ARES}$ parameter space. Our resultsdemonstrate the astrophysical constraints that can be expected for global 21-cmexperiments for a range of noise levels from pessimistic to optimistic, andalso the potential for probing redshift evolution of SFE parameters byincluding UVLF data.

Validating posteriors obtained by an emulator when jointly-fitting mock data of the global 21-cm signal and high-z galaxy UV luminosity function