  We present a simple method for evaluating the nonlinear biasing function of
galaxies from a redshift survey. The nonlinear biasing is characterized by the
conditional mean of the galaxy density fluctuation given the underlying mass
density fluctuation, or by the associated parameters of mean biasing and
nonlinearity (following Dekel & Lahav 1999). Using the distribution of galaxies
in cosmological simulations, at smoothing of a few Mpc, we find that the mean
biasing can be recovered to a good accuracy from the cumulative distribution
functions (CDFs) of galaxies and mass, despite the biasing scatter. Then, using
a suite of simulations of different cosmological models, we demonstrate that
the matter CDF is robust compared to the difference between it and the galaxy
CDF, and can be approximated for our purpose by a cumulative log-normal
distribution of 1+\delta with a single parameter \sigma. Finally, we show how
the nonlinear biasing function can be obtained with adequate accuracy directly
from the observed galaxy CDF in redshift space. Thus, the biasing function can
be obtained from counts in cells once the rms mass fluctuation at the
appropriate scale is assumed a priori. The relative biasing function between
different galaxy types is measurable in a similar way. The main source of error
is sparse sampling, which requires that the mean galaxy separation be smaller
than the smoothing scale. Once applied to redshift surveys such as PSCz, 2dF,
SDSS, or DEEP, the biasing function can provide valuable constraints on galaxy
formation and structure evolution.
