  Discrete-state stochastic models have become a well-established approach to
describe biochemical reaction networks that are influenced by the inherent
randomness of cellular events. In the last years severalmethods for accurately
approximating the statistical moments of such models have become very popular
since they allow an efficient analysis of complex networks. We propose a
generalized method of moments approach for inferring the parameters of reaction
networks based on a sophisticated matching of the statistical moments of the
corresponding stochastic model and the sample moments of population snapshot
data. The proposed parameter estimation method exploits recently developed
moment-based approximations and provides estimators with desirable statistical
properties when a large number of samples is available. We demonstrate the
usefulness and efficiency of the inference method on two case studies. The
generalized method of moments provides accurate and fast estimations of unknown
parameters of reaction networks. The accuracy increases when also moments of
order higher than two are considered. In addition, the variance of the
estimator decreases, when more samples are given or when higher order moments
are included.
