  Stochastic models are necessary for the realistic description of an
increasing number of applications. The ability to identify influential
parameters and variables is critical to a thorough analysis and understanding
of the underlying phenomena. We present a new global sensitivity analysis
approach for stochastic models, i.e., models with both uncertain parameters and
intrinsic stochasticity. Our method relies on an analysis of variance through a
generalization of Sobol' indices and on the use of surrogate models. We show
how to efficiently compute the statistical properties of the resulting indices
and illustrate the effectiveness of our approach by computing first order
Sobol' indices for two stochastic models.
