Optimization of stochastic systems via simulation
WSC '89 Proceedings of the 21st conference on Winter simulation
Likelihood ratio derviative estimators for stochastic systems
WSC '89 Proceedings of the 21st conference on Winter simulation
Convergence rates for steady-state derivative estimators
Annals of Operations Research - Special issue on sensitivity analysis and optimization of discrete event systems
Estimating security price derivatives using simulation
Management Science
Parameter estimation in flow through partially saturated porous materials
Journal of Computational Physics
BioSimWare: a software for the modeling, simulation and analysis of biological systems
CMC'10 Proceedings of the 11th international conference on Membrane computing
Grid computing for sensitivity analysis of stochastic biological models
PaCT'11 Proceedings of the 11th international conference on Parallel computing technologies
Journal of Computational Physics
Multiparameter Spectral Representation of Noise-Induced Competence in Bacillus Subtilis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Sensitivity analysis quantifies the dependence of a system's behavior on the parameters that could possibly affect the dynamics. Calculation of sensitivities of stochastic chemical systems using Kinetic Monte Carlo and finite-difference-based methods is not only computationally intensive, but direct calculation of sensitivities by finite-difference-based methods of parameter perturbations converges very poorly. In this paper we develop an approach to this issue using a method based on the Girsanov measure transformation for jump processes to smooth the estimate of the sensitivity coefficients and make this estimation more accurate. We demonstrate the method with simple examples and discuss its appropriate use.