  This paper presents a novel approach for augmenting proof-based verification
with performance-style analysis of the kind employed in state-of-the-art model
checking tools for probabilistic systems. Quantitative safety properties
usually specified as probabilistic system invariants and modeled in proof-based
environments are evaluated using bounded model checking techniques.
  Our specific contributions include the statement of a theorem that is central
to model checking safety properties of proof-based systems, the establishment
of a procedure; and its full implementation in a prototype system (YAGA) which
readily transforms a probabilistic model specified in a proof-based environment
to its equivalent verifiable PRISM model equipped with reward structures. The
reward structures capture the exact interpretation of the probabilistic
invariants and can reveal succinct information about the model during
experimental investigations. Finally, we demonstrate the novelty of the
technique on a probabilistic library case study.
