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Precision cosmology has recently triggered new attention on the topic ofapproximate methods for the clustering of matter on large scales, whosefoundations date back to the period from late '60s to early '90s. Indeed,although the prospect of reaching sub-percent accuracy in the measurement ofclustering poses a challenge even to full N-body simulations, an accurateestimation of the covariance matrix of clustering statistics, not to mentionthe sampling of parameter space, requires usage of a large number (hundreds inthe most favourable cases) of simulated (mock) galaxy catalogs. Combination offew N-body simulations with a large number of realizations performed withapproximate methods gives the most promising approach to solve these problemswith a reasonable amount of resources. In this paper I review this topic,starting from the foundations of the methods, then going through the pioneeringefforts of the '90s, and finally presenting the latest extensions and a fewcodes that are now being used in present-generation surveys and thoroughlytested to assess their performance in the context of future surveys.

Approximate methods for the generation of dark matter halo catalogs in the age of precision cosmology