  Motivated by applications to stochastic programming, we introduce and study
the expected-integral functionals, which are mappings given in an integral form
depending on two variables, the first a finite dimensional decision vector and
the second one an integrable function. The main goal of this paper is to
establish sequential versions of Leibniz's rule for regular subgradients by
employing and developing appropriate tools of variational analysis.
