  Cosmologists at the Institute of Computational Cosmology, Durham University,
have developed a state of the art model of galaxy formation known as Galform,
intended to contribute to our understanding of the formation, growth and
subsequent evolution of galaxies in the presence of dark matter. Galform
requires the specification of many input parameters and takes a significant
time to complete one simulation, making comparison between the model's output
and real observations of the Universe extremely challenging. This paper
concerns the analysis of this problem using Bayesian emulation within an
iterative history matching strategy, and represents the most detailed
uncertainty analysis of a galaxy formation simulation yet performed.
