  Let X_1 ,..., X_n be a collection of binary valued random variables and let f
: {0,1}^n -> R be a Lipschitz function. Under a negative dependence hypothesis
known as the {\em strong Rayleigh} condition, we show that f - E f satisfies a
concentration inequality generalizing the classical Gaussian concentration
inequality for sums of independent Bernoullis: P (S_n - E S_n > a) < exp (-2
a^2 / n). The class of strong Rayleigh measures includes determinantal
measures, weighted uniform matroids and exclusion measures; some familiar
examples from these classes are generalized negative binomials and spanning
tree measures. For instance, the number of vertices of odd degree in a uniform
random spanning tree of a graph satisfies a Gaussian concentration inequality
with n replaced by |V|, the number of vertices. We also prove a continuous
version for concentration of Lipschitz functionals of a determinantal point
process.
