First-order perturbation analysis of a simple multi-class finite source queue
Performance Evaluation
The asymptotic efficiency of simulation estimators
Operations Research
Application of smoothed probabilistic analysis to perturbation routing
Mathematics and Computers in Simulation - Special issue on stochastic systems modeling
Maximal coupling and rare perturbation sensitivity analysis
Queueing Systems: Theory and Applications
Principles of Discrete Event Simulation
Principles of Discrete Event Simulation
An Algorithmic Approach for Sensitivity Analysis of Perturbed Quasi-Birth-and-Death Processes
Queueing Systems: Theory and Applications
Hi-index | 0.98 |
Derivative estimation is an important problem in performance analysis of discrete event dynamic systems. Derivative estimation of stationary performance measures is difficult since it generally requires the consistency of estimators. This paper proposes an algorithm for derivative estimation of stationary performance measures for Markov chains. Both discrete-time and continuous-time chains are considered. The basic idea is to simulate the original Markov chain with a modified performance measure which can be estimated by extra simulations. The computational load of the extra simulations at each step is bounded. It is shown under mild assumptions that the algorithm attains the best possible rate of convergence as the simulation time goes to infinity. An unexpected connection between the algorithm and solutions to Poisson equations is also revealed.