  We consider the problem of bounding mean first passage times for a class of
continuous-time Markov chains that captures stochastic interactions between
groups of identical agents. The quantitative analysis of such probabilistic
population models is notoriously difficult since typically neither state-based
numerical approaches nor methods based on stochastic sampling give efficient
and accurate results. Here, we propose a technique that extends recently
developed methods using semi-definite programming to determine bounds on mean
first passage times. We further apply the technique to hybrid models and
demonstrate its accuracy and efficiency for some examples from biology.
