In a previous paper we introduced a new variance-reduction technique for regenerative simulations based on permuting regeneration cycles. In this paper we apply this idea to new classes of estimators. In particular, we derive permuted versions of likelihood-ratio derivative estimators for steady-state performance measures, importance-sampling estimators of the mean cumulative reward until hitting a set of states, and Tin estimators for steady-state ratio formulas. Empirical results are presented showing that modest to significant variance reductions can be obtained.
All Science Journal Classification (ASJC) codes
- General Computer Science
- Modeling and Simulation
- Management Science and Operations Research
- Information Systems and Management
- Regenerative method
- Variance reduction