A finite difference method for estimating second order parameter   sensitivities of discrete stochastic chemical reaction networks
  We present an efficient finite difference method for the approximation of second derivatives, with respect to system parameters, of expectations for a class of discrete stochastic chemical reaction networks. The method uses a coupling of the perturbed processes that yields a much lower variance than existing methods, thereby drastically lowering the computational complexity required to solve a given problem. Further, the method is simple to implement and will also prove useful in any setting in which continuous time Markov chains are used to model dynamics, such as population processes. We expect the new method to be useful in the context of optimization algorithms that require knowledge of the Hessian. 
Elizabeth Skubak Wolf and David F. Anderson
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