Permuted Estimators for Regenerative Simulations
نویسندگان
چکیده
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 large classes of other estimators. In particular, we derive permuted versions of likelihood-ratio derivative estimators for steady-state performance measures, importance-sampling estima-tors of the mean cumulative reward until hitting a set of states, and Tin estimators for steady-state ratio formulas. Empirical results are presented which show signiicant variance reductions in some cases.
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