Uroš Seljak investigates an analytic model to compute the nonlinear power spectrum of dark matter, galaxies, and their cross-correlation. The model is based on Press-Schechter halos, which have realistic dark matter profiles. The total power spectrum is the sum of two contributions: one from correlations between halos and one from correlations within the same halo. The model predicts dark matter power spectra that match well with N-body simulations, provided that the concentration parameter decreases with halo mass. The galaxy power spectrum differs from the dark matter power spectrum because the number of galaxies per halo does not scale with halo mass, and most halos contain a central galaxy. If the number of galaxies increases less rapidly than the halo mass, as predicted by theoretical models and observed in clusters, the resulting power spectrum becomes a power law with a slope close to the observed over several orders of magnitude in scale. This model also predicts a later onset of nonlinear clustering compared to dark matter, which is necessary to reconcile CDM models with data. The generic prediction is that bias is scale-dependent and nonmonotonic, particularly for red or elliptical galaxies, which are preferentially found in larger mass halos. The model's predictions for galaxy-dark matter correlations, which can be observed through galaxy-galaxy lensing, show that these cannot be simply interpreted as an average halo profile of a typical galaxy. Different halo masses dominate at different scales, and larger halos host more than one galaxy. The model predicts a cross-correlation coefficient as a function of scale and discusses the prospects of using cross-correlations in combination with galaxy clustering to determine the dark matter power spectrum.Uroš Seljak investigates an analytic model to compute the nonlinear power spectrum of dark matter, galaxies, and their cross-correlation. The model is based on Press-Schechter halos, which have realistic dark matter profiles. The total power spectrum is the sum of two contributions: one from correlations between halos and one from correlations within the same halo. The model predicts dark matter power spectra that match well with N-body simulations, provided that the concentration parameter decreases with halo mass. The galaxy power spectrum differs from the dark matter power spectrum because the number of galaxies per halo does not scale with halo mass, and most halos contain a central galaxy. If the number of galaxies increases less rapidly than the halo mass, as predicted by theoretical models and observed in clusters, the resulting power spectrum becomes a power law with a slope close to the observed over several orders of magnitude in scale. This model also predicts a later onset of nonlinear clustering compared to dark matter, which is necessary to reconcile CDM models with data. The generic prediction is that bias is scale-dependent and nonmonotonic, particularly for red or elliptical galaxies, which are preferentially found in larger mass halos. The model's predictions for galaxy-dark matter correlations, which can be observed through galaxy-galaxy lensing, show that these cannot be simply interpreted as an average halo profile of a typical galaxy. Different halo masses dominate at different scales, and larger halos host more than one galaxy. The model predicts a cross-correlation coefficient as a function of scale and discusses the prospects of using cross-correlations in combination with galaxy clustering to determine the dark matter power spectrum.