Stochastic process models naturally describe a broad range of individual-based phenomena and are increasingly being applied in ecology. However, the estimation of parameters in such models is an important issue which has typically received much less attention than the exploration of model behaviour. The difficulties of parameter estimation are compounded by the fact that in most situations the available data is in-complete in some sense. Here we demonstrate how two methods of Markov chain Monte Carlo (MCMC) Gibbs sampling can be combined within a reversible-jump MCMC framework to produce a hybrid sampler which can be used to obtain estimates of parameters and missing data for broad class of stochastic process rate models. We apply these methods to two stochastic models arising from the ecology of grazed ecosystems in order to display the benefits of the hybrid sampler and the usefulness of a stochastic modelling approach to experiments where limited data exists.