  We present a simple and accurate method to constrain galaxy bias based on the
distribution of counts in cells. The most unique feature of our technique is
that it is applicable to non-linear scales, where both dark matter statistics
and the nature of galaxy bias are fairly complex. First, we estimate the
underlying continuous distribution function from precise counts-in-cells
measurements assuming local Poisson sampling. Then a robust, non-parametric
inversion of the bias function is recovered from the comparison of the
cumulative distributions in simulated dark matter and galaxy catalogs.
Obtaining continuous statistics from the discrete counts is the most delicate
novel part of our recipe. It corresponds to a deconvolution of a (Poisson)
kernel. For this we present two alternatives: a model independent algorithm
based on Richardson-Lucy iteration, and a solution using a parametric skewed
lognormal model. We find that the latter is an excellent approximation for the
dark matter distribution, but the model independent iterative procedure is more
suitable for galaxies. Tests based on high resolution dark matter simulations
and corresponding mock galaxy catalogs show that we can reconstruct the
non-linear bias function down to highly non-linear scales with high precision
in the range of $-1 \le \delta \le 5$. As far as the stochasticity of the bias,
we have found a remarkably simple and accurate formula based on Poisson noise,
which provides an excellent approximation for the scatter around the mean
non-linear bias function. In addition we have found that redshift distortions
have a negligible effect on our bias reconstruction, therefore our recipe can
be safely applied to redshift surveys.
