  We introduce methods which allow observed galaxy clustering to be used
together with observed luminosity or stellar mass functions to constrain the
physics of galaxy formation. We show how the projected two-point correlation
function of galaxies in a large semi-analytic simulation can be estimated to
better than ~10% using only a very small subsample of the subhalo merger trees.
This allows measured correlations to be used as constraints in a Monte Carlo
Markov Chain exploration of the astrophysical and cosmological parameter space.
An important part of our scheme is an analytic profile which captures the
simulated satellite distribution extremely well out to several halo virial
radii. This is essential to reproduce the correlation properties of the full
simulation at intermediate separations. As a first application, we use
low-redshift clustering and abundance measurements to constrain a recent
version of the Munich semi-analytic model. The preferred values of most
parameters are consistent with those found previously, with significantly
improved constraints and somewhat shifted "best" values for parameters that
primarily affect spatial distributions. Our methods allow multi-epoch data on
galaxy clustering and abundance to be used as joint constraints on galaxy
formation. This may lead to significant constraints on cosmological parameters
even after marginalising over galaxy formation physics.
