Unbiased Estimation of Second-Order Parameter Sensitivities for Stochastic Reaction Networks

Abstract. Stochastic models for chemical reaction networks are increasingly popular in systems and synthetic biology. These models formulate the reaction dynamics as continuous-time Markov chains (CTMCs) whose propensities are parametrized by a vector [Formula: see text] and parameter sensitivities are introduced as derivatives of their expected outputs with respect to components of the parameter vector. Sensitivities characterize key properties of the output like robustness and are also at the heart of numerically efficient optimization routines like Newton-type algorithms used in parameter inference and the design of control mechanisms. Currently the only unbiased estimator for second-order sensitivities is based on the Girsanov transform, and it often suffers from high estimator variance. We develop a novel estimator for second-order sensitivities by first rigorously deriving an integral representation of these sensitivities. We call the resulting method the double Bernoulli path algorithm and illustrate its efficiency through numerical examples.