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Galaxy formation during the first billion years of our Universe remains achallenging problem at the forefront of astrophysical cosmology. Although these$z \geq 6$ galaxies are likely responsible for the last major phase change ofour Universe, the epoch of reionization (EoR), detailed studies are possibleonly for relatively rare, bright objects. Characterizing the fainter galaxieswhich are more representative of the population as a whole is currently donemainly through their non-ionizing UV luminosity function (LF). Observing thefaint end of the UV LFs is nevertheless challenging, and current estimates candiffer by orders of magnitude.  Here we propose a methodology to combine disparate high-$z$ UV LF data setsin a Bayesian framework: Bayesian Data Averaging (BDA). Using a flexible,physically-motivated galaxy model, we compute the relative evidence of various$z=6$ UV LFs within the magnitude range $-20 \leq M_{\rm UV} \leq -15$ which iscommon to the data sets. Our model, based primarily on power-law scalings ofthe halo mass function, naturally penalizes systematically jagged data pointsas well as mis-estimated errors. We then use the relative evidence to weigh theposteriors obtained from disparate LF observations during the EoR, $6 \leq z\leq 10$. The resulting LFs suggest that the star formation rate density (SFRD)integrated down to a UV magnitude of -17 represent $60.9^{+11.3}_{-9.6}\%$ /$28.2^{+9.3}_{-10.1}\%$ / $5.7^{+4.5}_{-4.7}\%$ of the total SFRD at redshifts6 / 10 / 15. The BDA framework we introduce enables galaxy models to leveragemultiple, analogous observational data sets.

Combining high-z galaxy luminosity functions with Bayesian evidence