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Learning How Galaxies Form Using The Bayesian Approach Based Semi-Analytic Model

Galaxy formation involves complicated processes. To disentangle the problem of galaxy formation, astronomers have developed Semi-Analytic Models (SAMs) of galaxy formation, which are multi-parameter phenomenological models that use parameterizations to describe the important physical processes. By adjusting the free parameters given constraints from observations, one hopes to learn about the effects of the dominant physical mechanisms of galaxy formation. To build SAM onto a probabilistic footing, a group of astronomers at UMass have been constructing a Bayesian model inference framework for modeling galaxy formation. Following an brief introduction of the Bayesian approach base SAM, I will show a couple of cases of how we learn about the processes in galaxy formation using the method.