  In this paper, probabilistic guarantees for constraint sampling of multistage
robust convex optimization problems are derived. The dynamic nature of these
problems is tackled via the so-called scenario-with-certificates approach. This
allows to avoid the conservative use of explicit parametrizations through
decision rules, and provides a significant reduction of the sample complexity
to satisfy a given level of reliability. An explicit bound on the probability
of violation is also given. Numerical results dealing with a multistage
inventory management problem show the efficacy of the proposed approach.
