Semi-analytic models of galaxy formation and evolution (SAMs) use relatively simple, but physically motivated parameterized prescriptions for the effects of complex processes such as merging, gas accretion, star formation, and feedback to follow a simulated population of galaxies through cosmic time. Though inexact, SAMs are necessary since direct simulation of these processes is still beyond the reach of computational hardware and techniques. The disadvantage of SAMs is that the degeneracies among the adjustable parameters can be large and poorly understood. In response, the proposing team has developed a Bayesian inference based SAM. The Bayesian approach yields the posterior probability distribution of the SAM parameters for a given set of data, and an assessment of how well a particular model is supported by the data. In this project, the team will apply their analysis to archival data on high-redshift galaxies, and address the following issues: (1) how galaxies assemble their masses, how star formation proceeds in different galaxies, and whether a universal stellar initial mass function is consistent with the data; (2) how feedback affects the gas component in galaxies, and why most of the baryons today are not in galaxies; (3) the growth of supermassive black holes, and how feedback from active galactic nuclei (AGN) affects galaxy formation and evolution; (4) the properties of ionizing sources at high redshift and how the Universe was re-ionized; (5) whether the Cold Dark Matter (CDM) paradigm can accommodate all observational properties of galaxies. These models will be used to create mock galaxy catalogs tailored to the properties of specific galaxy surveys; the team will make these mock catalogs available to the community. The project will support the work and professional training of a graduate student, and is intended to provide research opportunities for undergraduates.